{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"variational_autoencoders.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.8"}},"cells":[{"cell_type":"markdown","metadata":{"id":"2Vn26lahhFJd"},"source":["# EECS 498-007/598-005 Assignment 6-1: Variational AutoEncoders\n","\n","Before we start, please put your name and UMID in following format\n","\n",": Firstname LASTNAME, #00000000   //   e.g.) Justin JOHNSON, #12345678"]},{"cell_type":"markdown","metadata":{"id":"ZsZYgVWdALCH"},"source":["**Your Answer:**   \n","Soufian BENAMARA, #NOid"]},{"cell_type":"markdown","metadata":{"id":"VJZ8AefthL95"},"source":["\n","# Variational Autoencoder\n","\n","In this notebook, you will implement a variational autoencoder and a conditional variational autoencoder with slightly different architectures and apply them to the popular MNIST handwritten dataset. Recall from lecture (https://web.eecs.umich.edu/~justincj/slides/eecs498/FA2020/598_FA2020_lecture19.pdf), an autoencoder seeks to learn a latent representation of our training images by using unlabeled data and learning to reconstruct its inputs. The *variational autoencoder* extends this model by adding a probabilistic spin to the encoder and decoder, allowing us to sample from the learned distribution of the latent space to generate new images at inference time."]},{"cell_type":"markdown","metadata":{"id":"JtA1_PsYhs24"},"source":["## Setup Code\n","Before getting started, we need to run some boilerplate code to set up our environment, same as previous assignments. You'll need to rerun this setup code each time you start the notebook.\n","\n","First, run this cell load the autoreload extension. This allows us to edit .py source files, and re-import them into the notebook for a seamless editing and debugging experience.\n"]},{"cell_type":"code","metadata":{"id":"OKXSEQjRh63r","executionInfo":{"status":"ok","timestamp":1615482509288,"user_tz":-60,"elapsed":1207,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}}},"source":["%load_ext autoreload\n","%autoreload 2"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"eIXWSou6h_S6"},"source":["### Google Colab Setup\n","Next we need to run a few commands to set up our environment on Google Colab. If you are running this notebook on a local machine you can skip this section.\n","\n","Run the following cell to mount your Google Drive. Follow the link, sign in to your Google account (the same account you used to store this notebook!) and copy the authorization code into the text box that appears below."]},{"cell_type":"code","metadata":{"id":"evqEGDXRipC-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615482530091,"user_tz":-60,"elapsed":18934,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"20bb9533-e89f-4ae2-a4b3-f84f1dded8d8"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2y_JbZTlDoai"},"source":["Now recall the path in your Google Drive where you uploaded this notebook, fill it in below. If everything is working correctly then running the folowing cell should print the filenames from the assignment:\n","\n","```\n","['eecs598', 'gan.py', 'generative_adversarial_networks.ipynb', 'a6_helper.py', 'vae.py', 'variational_autoencoders.ipynb']\n","```"]},{"cell_type":"code","metadata":{"id":"Z8OU2pRkivyc","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615482535690,"user_tz":-60,"elapsed":1748,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"e8ce0af1-1359-4690-fbb0-988b64c135f1"},"source":["import os\n","\n","# TODO: Fill in the Google Drive path where you uploaded the assignment\n","# Example: If you create a 2020FA folder and put all the files under A6 folder, then '2020FA/A6'\n","GOOGLE_DRIVE_PATH_AFTER_MYDRIVE = '2020FA/A6'\n","GOOGLE_DRIVE_PATH = os.path.join('drive', 'My Drive', GOOGLE_DRIVE_PATH_AFTER_MYDRIVE)\n","print(os.listdir(GOOGLE_DRIVE_PATH))"],"execution_count":3,"outputs":[{"output_type":"stream","text":["['generative_adversarial_networks.ipynb', 'gan.py', 'eecs598', '__pycache__', 'a6_helper.py', 'vae_generation.jpg', 'vae.py', 'variational_autoencoders.ipynb']\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"GJ7auXOMi4rw"},"source":["Once you have successfully mounted your Google Drive and located the path to \n","this assignment, run the following cell to allow us to import from the `.py` files of this assignment. If it works correctly, it should print the message:\n","\n","```\n","Hello from vae.py!\n","Hello from a6_helper.py!\n","```\n","\n","as well as the last edit time for the file `vae.py`."]},{"cell_type":"code","metadata":{"id":"5zOOPYIUjCUO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615482546118,"user_tz":-60,"elapsed":6440,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"e46c68d4-f854-450b-8c77-e46e3f241ce7"},"source":["import sys\n","sys.path.append(GOOGLE_DRIVE_PATH)\n","\n","import time, os\n","os.environ[\"TZ\"] = \"US/Eastern\"\n","time.tzset()\n","\n","from vae import hello_vae\n","hello_vae()\n","\n","from a6_helper import hello_helper\n","hello_helper()"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Hello from vae.py!\n","Hello from a6_helper.py!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"JuIiv2bhjFoC"},"source":["Load several useful packages that are used in this notebook:"]},{"cell_type":"code","metadata":{"id":"sLdT7GSljI0f","executionInfo":{"status":"ok","timestamp":1615482554756,"user_tz":-60,"elapsed":5444,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}}},"source":["from eecs598.grad import rel_error\n","from eecs598.utils import reset_seed\n","import math\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","from torch.nn import init\n","import torchvision\n","import torchvision.transforms as T\n","import torch.optim as optim\n","from torch.utils.data import DataLoader\n","from torch.utils.data import sampler\n","import torchvision.datasets as dset\n","\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","\n","\n","# for plotting\n","plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n","plt.rcParams['font.size'] = 16\n","plt.rcParams['image.interpolation'] = 'nearest'\n","plt.rcParams['image.cmap'] = 'gray'"],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"_nqWhiLojS8M"},"source":["We will use GPUs to accelerate our computation in this notebook. Run the following to make sure GPUs are enabled:"]},{"cell_type":"code","metadata":{"id":"RdQhVgi5jVQp","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615482558046,"user_tz":-60,"elapsed":921,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"12ac3726-377b-4ad7-b781-5c7b372fe584"},"source":["if torch.cuda.is_available():\n","  print('Good to go!')\n","else:\n","  print('Please set GPU via Edit -> Notebook Settings.')"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Good to go!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"bcqRQILRjchz"},"source":["## Load MNIST Dataset\n","\n","\n","VAEs (and GANs as you'll see in the next notebook) are notoriously finicky with hyperparameters, and also require many training epochs. In order to make this assignment approachable, we will be working on the MNIST dataset, which is 60,000 training and 10,000 test images. Each picture contains a centered image of white digit on black background (0 through 9). This was one of the first datasets used to train convolutional neural networks and it is fairly easy -- a standard CNN model can easily exceed 99% accuracy. \n","\n","To simplify our code here, we will use the PyTorch MNIST wrapper, which downloads and loads the MNIST dataset. See the [documentation](https://github.com/pytorch/vision/blob/master/torchvision/datasets/mnist.py) for more information about the interface. The default parameters will take 5,000 of the training examples and place them into a validation dataset. The data will be saved into a folder called `MNIST_data`. "]},{"cell_type":"code","metadata":{"id":"mExnwvTXjcF_","executionInfo":{"status":"ok","timestamp":1615483398658,"user_tz":-60,"elapsed":695,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}}},"source":["batch_size = 128\n","\n","mnist_train = dset.MNIST('./MNIST_data', train=True, download=True,\n","                           transform=T.ToTensor())\n","loader_train = DataLoader(mnist_train, batch_size=batch_size,\n","                          shuffle=True, drop_last=True, num_workers=2)"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CwDmYBjdhTrM"},"source":["## Visualize dataset"]},{"cell_type":"markdown","metadata":{"id":"Q2X_21cTwsox"},"source":["It is always a good idea to look at examples from the dataset before working with it. Let's visualize the digits in the MNIST dataset. We have defined the function `show_images` in `a6_helper.py` that we call to visualize the images.\n"]},{"cell_type":"code","metadata":{"id":"3JMbbxMkwrYg","colab":{"base_uri":"https://localhost:8080/","height":629},"executionInfo":{"status":"ok","timestamp":1615483411748,"user_tz":-60,"elapsed":6157,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"aded9491-97ca-4055-c15d-69955eb2e67b"},"source":["from a6_helper import show_images\n","\n","imgs = loader_train.__iter__().next()[0].view(batch_size, 784)\n","show_images(imgs)"],"execution_count":21,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAqoAAAJkCAYAAADZZwOuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydecBU4/vGP0USKqWoREQRSUSWlJQSqahQtClbtsieyhKFpMWSIvSVEIkWlSxF1khFdllStpBoQ/X7Y37Xc2bOO/O+M/POzDlT9+eft2Z9njnrc933fd0ltmzZgmEYhmEYhmGEjZJBD8AwDMMwDMMw4mE3qoZhGIZhGEYosRtVwzAMwzAMI5TYjaphGIZhGIYRSuxG1TAMwzAMwwgldqNqGIZhGIZhhJLtC3uyRIkSW5V31ZYtW0pE/9/ml1/Y/PIbm19+Y/PLb2x++c22Nr9oTFE1DMMwDMMwQondqBqGYRiGYRihxG5UDcMwDMMwjFBiN6qGYRiGYRhGKLEbVcMwDMMwDCOUFFr1b6TGq6++CsBTTz0V8/iwYcMAeOyxxwAYP348AO+//37uBmcYhmEYhpFnlNiyJbHDQRjsD26++WYAbrrpJgBOOOEEAObOnZvyZ2XT3qFPnz7uhrRkycKF6h9//BGAl156yb3n448/LvYYtjX7CptffpGP89ttt90AGDFiBADnnHNOwuM7H+eXCja//Mbml99sa/OLxkL/hmEYhmEYRigJfej/+OOPj/n/a6+9BhRPWc0G1atXL1JJFVWrVgWge/funHbaaQBUqFAha2MzDCM9mjdvDsDZZ58NQGERKCMzlCpVCoBq1aoB0KtXr5jnu3fvDsDee+/tHnvwwQcBL/1q6tSpCT//n3/+AYLflvvssw8ALVq0AKBRo0YAvPXWWwAsXryY2267DfD2wyeffBKIKPuGsa1giqphGIZhGIYRSkKtqL722ms0bdo07nN6PCyKajwWLVoEwC677AJAmTJlANhzzz3da8qXLw/AkCFDALjhhhtyOcSssttuu3HccccB0K5dOyCiPIOnIkjVeOONNwA4/fTT+f3333M9VMOIoW7dugAMHz485vFx48YFMZytngoVKnDdddcB0LZtWwAOOOCAQt+zefNm9+8LLrgg5m9h7LjjjgD8+++/aY21uHTt2hXAqaU6J65YsQKAzp07A5H57bDDDoB3ntR5Mx/Qde+YY44BvKiEaNOmDRMnTgTg8ssvj3lOEcZbb70VgPvuuw+Azz//PHsDLgLdc9x1110AHHHEEQCUKBFJrVyyZAkADz30EBApnv77779zPMrcUbZsWWrWrAnARRddBHiRDVGjRg0AfvjhBwAWLlyY1neZomoYhmEYhmGEklAqqlq5JFJTw8jo0aN54YUXYh77/vvvAdyquFKlSgBMmzYN8CqKASpXrhzzWuVR5QMau/KJO3ToAES23x577AF46odUA61K169fD3iK6po1a3I06m0PrfxPOukkAM4880wAzjjjDMDLjbvxxhuBWPs05QtqGz/77LNAcKpUMkiZevvttwH49ttvi3zPoYceCsD06dMBqFKlCuD9FpdccklGxzhp0iTAO2YS8euvvzJy5MiMfjdEnEcAPvjgg4x/djLo973ttts499xzY57btGkTAH/++ScAEyZMAGK348UXXwzA/vvvX+j3bNiwAYCXX345RokNggceeADwjp1rr70W8CJtffr0AWDt2rU88sgjAPTo0SPHo0yN0qVLs9deewGecqrtqVxinfvfe+89AN59990C2035yUuXLgW8eo5nnnkGyL2iqrqTQYMG0bdvX8A7ZhVt+fLLLwHo1KkT4NlRtm/fnt69ewPBKsGZonTp0gB07NgRgCuuuILDDjss5jWKaPjzv+V0VK9ePf7444+Uv9sUVcMwDMMwDCOUhEpRlYIqz9R8YtmyZSxbtqzQ13z11VdAJDcHIivO888/H4D99tsPgJ133hkIt6IqpapevXqAV5UrxTiasWPHAl4ThKByinfddVcgklcTzeGHHw5Ecmf079q1awOe2iO0KtTK+bDDDnMrbqk0qtx95513Mj6H4lC3bl0GDBgAeEqqVr1Srlq2bAl46lS9evVcDrUUAeWdKSoQRkVV+d5XXnkl4CkcUooL49577wU8Bfm7774DvH0+0/OV2tu+fftCX1epUiUGDRoU9zkp5elUsUu98+/ruUL7ZLSa+t9//wG4+SqXMx5SZx599NG4z7/++usA9O/fH4A333yzmCNOn6OPPhrwlCkpcto//VGn+vXrs/32kUt0WKv8dd3q37+/c2MQUh6lRE6ZMiXm+RYtWlC/fv2Yx1q1agV4+6POuR999FGGR144OqZuueUWAK655hp3X3L33XcDBc8Firoo2jRu3Dj3G5x11lkAfPbZZ1keeeZRxG3gwIEA1KlTJ+XPkDJev35959yUCqaoGoZhGIZhGKEkVIqqVizJ5KaqY1U+IrXt/fffZ/fddwe8PJd08jdyhRQAVWiuXLkS8NQ2zWvy5MkAPPHEE06ty6VnYdmyZZ0ao5Wccg/9OVGqQmzQoEHCMSZSrLZs2eJUkKA9Gf1I6VXu49ixY52qLFcFqUzyntRf+TtWqVLFbUspqVIYpXqFhXLlyjklVXlhixcvBmDmzJlFvl+5gI0bNwa8iIb2o6KiJemiiEOzZs1ivld5mYpSnHLKKRn93nXr1gFetW6YkMKYSEmVe0qjRo2cuuVHquTgwYOBYJVUoRzO7bbbLu7z8tTW8fnbb785xVG1Cz/99FO2h1ko3bp1A7zcYFW+r1u3jiuuuALwcnCLOvfPmTOHOXPmAJ6CquNB6PjL9XVRkc5+/foBkXxMRVuKQtfBVq1a8fTTTwOe928+uPro/C8v4gMPPBDw9tt423P06NEAzJo1C4AFCxYA3r2c3B369+9viqphGIZhGIax9RAKRVV32MkoqepItTVQqlQpp0xJuQorlStX5rzzzgM8hVG5K2HLxxw0aBCXXnopAF988QUAtWrVAgquBpWXmgzKJ5S/Y8WKFd1zv/32GwCrVq1Kc9SZQUqqKomlMoKnxsjTVtW3Qoqq8mynT5/uVtNCOWSqog4Lzz77rOves3HjRiDiyQuFV/srF1XnHr+vb6Lcx0yxdu1aALp06QJ4LhhSVqUeRndhUo6jcsaUh+ln9uzZ7jXKkROKXj3//PPFn0Qx0HETjY5Vqfjyotxpp50AGDVqFBCb1/rXX38BnouBntPvGwakHmr/POigg2Ke93fTKlWqlFOXtc2HDh2a7WHGRTnaI0aMALxIlJTVWbNmOZebdLjnnnsAnEuMIgp+J51cofOmtplU4lRYvnw5//vf/wAvOhBWRXW77bbjqquuAmKvGdHo2qJz05gxY1y0KlHUavXq1YB3z5Cuk1OgN6qp2FCpCCfMBv+pMmzYMFq3bg0UNMoNGxdffLEr9NIO/fHHHwc5pIQMGjTIhQR1ATv11FMBCiTvixIlSribFIVr/GE2XVRVkPXBBx+4Gx0Vi6lgLihkru0/2cybN88ZqSeyAJPx+L777lvgORk2F+dilA1OPPFE91fb7+qrrwaSs6PSzYGMqbWNlRaRK3Rh9qMb13j2NrKJSUTTpk0L2MfoN3n88cfTGGXmUTj14osvdibvKizT9pTpu0LL0Teo2pcVWi2sdWrQ6KL98ssvA55NnIpSZRgv9ttvP3dt9L83VzRs2BDwtpNSKFRQWtyFgFIHtM11/tS8lV6WK3R9KFeuHOCljiiNIRW2224711wi3oIsTFx11VVurolSNZTmpnswf5OGwtBnvvjii2mNz0L/hmEYhmEYRigJRFFVIVQyNlS6e9+aQv6idevWTk6PDiOHCVmqDBgwwJmCa3UdtoIaEW/1+sQTT8T8LQ4NGjQAIqturTIVcgwK7Ue33357zOMK7/fs2bOAkipzbYXvtK3jodQUFeEEjYrilMQPnh2OWhgmQpY/zZo1K9CmUypB2NJZ0qFv377Oak1IsQo6RUX8+uuvQMR+ya+2qBAwUTOEGTNmuP393XffzeIoM4uiE0plUFRNiqrC3wo7g9dCVMWMuUJNFqSIXX/99UBmUir22GMPp4TrPCrrvFwrqUIpFjqfalulw7Bhw7jsssuAcBYtghd9GjJkiNvGun6OGTMm5jX6bZKJxijq6G9pnExhazxMUTUMwzAMwzBCSU4V1XQM/WW4uzWh/J7KlSu7HDS1iAsLWj0piX79+vWuQCmsSmquuPPOO4FIcwbZ4MyYMSPIIbn8YbU4FSqqirZXkhr58MMPx7zn008/BbyVtAonoKBSGxTKJdXKvGbNmgD8/PPPzuC/qGYZsumKXt2r6O7JJ5/M7IADQNs32phb55lFixYFMqaieP31151S07Vr10Jfq/a23bt3d7mb+YSUfzVKkeotWyAdd9WqVXMWazrn5ApZaanNt5QxtTYtDsrNHTp0KHXr1gW84y7o/VNFolJ4leOdij2dajguvfRS16hA59qwcN111wGx91c6B8oKT/nsirzpWiLLsI4dO7riPr/9m2pCZLkm0i0aN0XVMAzDMAzDCCU5VVRTNXo94YQTtooqf62YZREj49wwW1IpL0U5Nrfffnte5YHlCq1Cw4rU+6OOOsrlY+ox5cjJvUF54GqrCfDhhx8CBK5cqVWjclL1f3H88ccnNENXzp+URkV0ZJkCnrq1NSCbMTlSgGeiH9ZGKevXr3f5kIkUVSmpPXr0AILfJ9NF17RvvvkG8JwrFBVRi+2NGzc6O7hc54bL8F5V62pIUxxkzaR9sFSpUs7mqmfPnsX+/EwgBVsNBpQzm8y1WudPKaqrVq3irbfeAsLTEEbXdSmpytdft26dU839bimy1pLziNThzZs3O+VdaB9W21yh3OZ0c+NNUTUMwzAMwzBCSc4U1VTU1Hz3TJU3nCr5pQLJn1LV13PmzHFVj0LvkRF9rn3z1KpPq3qpqKnkFW+tqNpfeUsLFy50/n9BI8VlypQpgGd2H6/a9N9//wXgjjvuAArmgUuxAs+PVe/JNRdeeCHgjbFy5cpAQYXihRdeSKhalC9fHvDa6ep1W7ZscblnUo7zEeWTK49Y8wWvkjysnseiSpUqRVYTS1ENc5vpVNB8lfOnBipqcHD22Wfzyy+/BDI2NZGQd206DT7ky6xjWHmRUvF+/vlnd/1TE4SwIEX52WefBSLnU51b/SiPVx6sLVu2BDynhjAgNxN/rrNcN0499VTef//9uO+Vw8Njjz0W8zcaKanad5Xjq4YiiVodJ4spqoZhGIZhGEYoybqiqnyUZLpPSTUJax5VYZQpU8a1nlSu1e677x73tVJAnnvuObfalEqnnA95WqqFYq5R5fSee+4JRPLdiuMptzXw448/AhElACKtVNX5KmjUOUWqjJQQ5V5+8cUXTl2TQ4E/51g5VlJCAObPn5/FUReNohFF5XgdcMABKeeBrVy5klmzZgFeVXw+IveCJk2axDy+dOlS59GpfTdsqOr7yiuvTHi+FHIdUb7g77//nt3BZQnlTCtXXPutv53utGnTAhhdBPkIq2OaahWuueYaoKDCuv3227tooPIR5RijXFx5wEp9O+WUUzLiIpAN1LpVavfkyZPdOVVdJHWNlO/o8OHDAahUqRIQOR4/+eST3A06DqrYVx609jVFJdTGN91j6bjjjgO8uUtJlee6jtniYoqqYRiGYRiGEUqypqim4pmqXNR8UlLl2ah8m9atWxdQNBIh77GePXu6nFDlX+l3C2olplXiwIEDAXj00UeBiCIn5UaVexrzggULABJWXW8t1KpVC/AUgbFjxwY5nLhIWVWVt/4mgxQDsXz58sAVY1V2y7tXOVXKp4rOvZQDg5Sb5cuXA3DPPfcAXjW5FJCDDjoo8PllAinhUjPEV199FVolVee9u+66C/C8NcE7n0RHc8BTX6UCBa32p4oiaRMnTgS8eQhFOKTmBYlyDZXbKG/lY489FihYGV6hQgUXUVSUQrnuOh5feeUVwDvPBO2ZmgyqeF+9erXLAZdirPO/5qfrv1+9DBKNWd6oQlG14kQlOnTo4O4TDj74YMDbpu3atQMyF8nJ2o1qKsVT+dQeVTfexSkuUsvKcuXKuRteGR7rZCZj86Dwt67t0qWLs+rQDZsu/Cq00Y2qQnN33HFHgbad+YhsnAYPHgx4BW9qR7m14L9wPvTQQ+6CFRQNGzYEvH0slRaSumC0bds25nEtsPL1JlUNGmQBoxtUXRhV7BDGZikKReoiH32DqhaqCn1rkayLXj6zww47MG/ePACOPPLIgEeTPCogUvh+wIABgGe5qMdnzJjBeeedBxQ0x1cagW6KgkxpSBUdW6NGjXK/xVFHHQV4N6a6HmqhoetfGBYcssrSuUE3jqNGjUr5s5SG2LFjRyBidaUbVAlrslOToJApLPRvGIZhGIZhhJKMK6rJFE1JrQvjir8olCidDDIU94cApMiB12ps3LhxgKfaSUkKGoV4brvtNhcGEQrFyRT6nHPOAbwE61GjRnHuuecC4QiDpIvsR7SSFkG3Tc02YWjrm45qre3lt2vSZymKkW+cfPLJADz11FOAl4IiFJWRqXcYDfF1XvOb+q9cudKdP7aGKIxQ+sLcuXNdu19ZoantZKYKTrKJQrodOnRI+j0lS0Z0MClxUszztXBRqUT6K+sqPwceeKD7t4qKwoJSpdRoIRlkt6nrnc6v4P0GUtOzFYEzRdUwDMMwDMMIJRlTVKWkJpObKiU1Xw39kyWRiqhigTp16rgcUBUVSLVUXk+Y8ZuIq8BFLTiHDx/ucmRUwJKPKCdQCrna7Mm6Jd+RArDPPvvEPC67nHxBCo6MtqUE6DiUhUq+bje1YPYrqcp9lBWZCiLDhKIR/ijESy+9BEQUmaKUVOXAB2WCnw46r9esWdPNT8VEOq8I5U5vLdSrVw+AQw45BMivYunioMgHBG+hpnOicm1VA6Nc8ehGLoo8KdqhfGRFkXUelaXYoEGDEqrKmcYUVcMwDMMwDCOUZFxRLQwpUlsrqoAUapkqxUMrFFXKzZs3j+222w4gtMbHhmftoRWlIgH5WjXuR2qXrNbyFblS6K9QG2I1D8gn5FjQr18/dz7xozxyWZOFETUeKFu2bMzjU6dOBWDFihXuMVX9q4JYyNZJNmRhRrm4arG8ceNGmjdvDnjOFX7ruHxu42t4RKuoma5+TxW/I4icCqSEKo+9RIkSHHPMMQAFzjOKYMhN5IYbbsjuoONgiqphGIZhGIYRSnImoWwt+aiqrFUlo1Sobt26uYpIVTU2btwYgO+//x7wDKrzzag6WaSYSz357rvv2LhxY5BDKhbyClRuoFalkyZNCmxM2UAm3kIKndrs5QvXXXddzP+//PJLAHr37h3EcIqFoi79+vUDvHzbaNTSWDnvYVZUk0EKqlwZlE8nZs+enfMxpYvawaqxwZ9//un+rbzc+vXrAzBixAjAi7wZ+U104w21rQ4Kqfd77713zOO6RosSJUq465vqSdQKXpGoIH3DTVE1DMMwDMMwQknGFFVV9KmSUTmr/g5H+c5bb70V81eoXVw0n3/+eS6GFBrkAauOQGPGjAm8s1FxqFq1asz/1dUjrG0p00XKv1BOUr5tOylT8vFVhxh/p5ww06ZNG8DryrTrrru659RGVvnsnTt3BvIjZ1NRpPfeew/wcjjV3vbOO+90Cqo/V3rkyJFAat0Og0ZRNalr5cuXd76pQr/J/fffn9vBGVmlQYMGQQ/Boba23bp1AyJOQ/EoUaKE87lVhy1/m9wgMUXVMAzDMAzDCCUlCusYVKJEifxtJxSHLVu2xNgO2PySp2zZsq7H7/jx4wGv68oFF1wAeNWAM2fOBCLV15nsQpLr7adcOVXnvv3224C3Ss00Qe2f6tOs1bZUL38nruJix19iDj/8cABmzZoFxHavE1KM1Xkq12Ri+8mrMRl/yXvvvReAvn37ArG5f9kgG/un5tCpUye3TbUdpaTmSvHP9fGnnFx1Z5JnZ7RfbIUKFQAvcuDvWJYKYTm/qFZl0qRJ7lqRCV/0sMwvW/jnF01++9EYOaNkyZIupKowggo8ZEA+atQoAPr37w/kb6s8P1rMaZ6y2Nla7Kl0wZRR/pFHHgnAhg0bXBMAma0buUUFUmPHjuWhhx4KeDTFR80WZMu3tXPZZZfF/N2WUOOJ888/H/AEDaUFfvbZZ+6a4W8ek89Ez0XtxY3iYaF/wzAMwzAMI5RY6H8rwuaXWRT6v/vuu/X9gFesk+k2t0FtP4XdZOgsU+jhw4e7uWdCHbf9s2hUXHT55ZcDXhqG30IsCGz75Tc2v/xmW5tfNKaoGoZhGIZhGKHEclQNowhk03TrrbcCsHDhwiCHk3HURu+0004LeCSGCof01zAMY1vHFFXDMAzDMAwjlBSao2oYhmEYhmEYQWGKqmEYhmEYhhFK7EbVMAzDMAzDCCWFFlNt7fYHNr/8wuaX39j88hubX35j88tvtrX5RWOKqmEYhmEYhhFK7EbVMAzDMAzDCCV2o2oYhmEYhmGEEjP8LyYXXXQRAPfff79rf7hy5Uog0oLSMAzDMAzDSA9TVA3DMAzDMIxQYopqmpxwwgkA3HbbbQBs2bKFrl27ArD99pGfdddddwXgjjvuAGD9+vW5HqZRBDVq1ABg3LhxNGvWDIBp06YB8Mcff8S89qOPPgJg5syZAHzyySe5GmYg7L777pQsGVnL/vvvvwD89ttvQQ7JMAzD2MYotDPV1m5/UJz5zZo1C4ATTzzRPXbQQQcBcOGFFwJwyimnAHDyyScD8O2336b7dUmRifmddNJJAFSuXBnwbtr+/PNPjjvuOADmz59fvIGmSSa3X6VKlQCYMmUKAI0aNUr6vWvXrgWgevXq/Pnnn+kOoQBB2Y/svffeANSvXx+ADh06ANC+fXt22mknwLtpv//++4H0Fl/ZnN+hhx5KkyZN4j43atQoADZv3hzzeJ8+fbjvvvsyNYRtzj7G5pcep556KuCdWzds2ADAZZddBsDDDz8MwF577UXr1q0BqFWrVsxnNGjQAIjswwCLFy8u8ntt++U329r8orHQv2EYhmEYhhFKAlFUjzjiCABee+01AHbZZRfAC6fPnTs3G1+bVUV1/Pjx9OrVK+Y1UiV//fXXdL8mJVKZn8LX/u1/9NFHA17awhtvvAFEVMRDDz0UKLh6HzFiBAALFiwA4O+//wa8cHGmyOT2mzRpEgCnnXYa4KkYUDD0r9/kxhtvBGC33XYD4Nlnn6VTp05AQbUuHXK9YpaCqvlWrVq1wGtKlCihscU8/sgjjwCeCrRx48Yivy+T89t5550Bb99r06aNU8n9aA5Lly4FvMjHqlWrmD59OgCXX345AOvWrUt3SGnNr3PnzgBMnDgRgLfeeguIqGkAEyZMcK/t3bs34B2b4tZbbwXgrrvuArzj7p9//kl1CoWyrSk6mZ6fziNz5swBYNmyZQBcccUVgHc9FJs2bSpw3EWNDYCzzjoLiJyLiiJs269bt25cc801gHdM6nozefJkAKZOnQrAd999V+Tn5Wp+ZcqUAbxjVKq2riWlSpUCItd/nV80z88++yzt783V/JQOp+iqqFOnDuDN/9FHH+Xdd9/N2PeaomoYhmEYhmHkHYEoqlJhRo4cGfN4w4YNAXj//fez8bUZWZEcfPDBgKce7rDDDgAMGzaM6667rthjLA6pzO/ee+8FIkoUeHmKmUA5gV999ZV77J133gGKt20zuaKUcnXYYYcBULNmTX788cdC36Mc3ddff909pt/thx9+SHcojlwrHlJSlUMdj0SKqrjhhhsAGDp0aJHfl8n57b///kB8hUL2cOPHjwfgzTffBODTTz8FPDW9QYMGbn61a9cG4Ouvv053SGnNT8fKpZdeGvO41FCNTypNPPQaRTJU9HfxxRezaNGi5AafBEErcu3atQMiSiNEzi/FUaj8ZHt+isT0798f8BRwKa1XX311zOuPPvpod9zp/KLPUO54PiqqQ4YMASJKsn+/9p9vdE7u168fAI8//njCz83m/KpVq+ZqT1q2bAl49yvJoO2n433YsGEpjyGb89tvv/1cMfjZZ58NeOfYRNx9991ce+21mRqCKaqGYRiGYRhG/pFTeyrlPMjSSWiVFE9tK126NADHHnss4OWQZTI3IhWaN28eMy4hs/98Qar2Y489Bngqk3j++ecBb9VYrVq1Ap+hPMHdd9895nHl+0WzfPlyAM444wzAU8LWrFmT1viLS/v27QH43//+B0Djxo0L/AZ+3n77bcDLo2rcuDFHHnkkkBlFNdfIlcKvlkqJHDRokKvqVw6Z8utEorzQIJBdmKIEifLaVq1albMxFcULL7wAQKtWrQCoUqUK4J0jpY7uscce7j1yqNA5UdtGx6MUuv333z+jimqu2GeffQCvOl5qc4UKFQAv9x/g5ZdfBry5CzmT6Lzz008/ARGlWr+XnCtyhazdBg4cCHjXPY1H+8K4ceMATyEHz11DEbztttsu5jPDzJ577gngFDupcIVFc4Xy5suXL5+l0SVH48aNnRKeCM3nv//+A2KjINWrVwciUQ5IT1HNJKeffjqAs2Rs166dG2OyNG/e3F37f/nll8wO0IcpqoZhGIZhGEYoyZmiWq5cOZeTWrZs2ZjntMqIhyqttZrRylhqQra9Sf3Iv06rJ+W/ZdJPM5d88MEHQCRHJVWUr6s8KSEXB1WzglchqVzVSy65BIDRo0en/L2ZQPuR8o0qVqxY5HuUGxftZnDggQdmYXS5QdEBuXDoWIqX7yalTzlkQrlyuUb5z9r3nn766UDGUVxeeeUVwMuRlRqqbaH9NBpFQYTOQcccc0zMe3Ss5Qu6LkhtkuqTiJkzZ7pjUful9mn9FvFQNC7Xiqo4/PDDAWjbti3gnYN1jUvGQSPMaFuoSly58KomTwdFwDLpe5wKTZs2dc4uynX//PPPAS/K9PPPPwOeMt6rVy93byOHlbCg+yddh9euXesU/quuugqAv/76C/DqEBQJEIcddpirb1AULluYomoYhmEYhmGEkpwpqp06dXKqgVCXm8JWkH7FSrmAv//+e4ZHWDjKj1JOh/jmm4/GLmUAACAASURBVG8Ar7vItoR8Kf0rLbWQHTt2rFNqtaoWeo98IrV6C4pk9ieph9E5uX7vw3xCfsWp+Bb788rk7amc51zz3HPPAZH8VOUuKm/Wn6N6/PHHA5F8s7CSigoqxwmpI9o2UnzyLW9a6pOUVOUyfvjhh4CXT37eeecBnhd0PKT0KKdTLF26NMaNJAiUd6ntJecRRQ/951PwfhM5joQ5N1W5/kUp4qmg4zwoypQp4/bDZKv9x40b5yKJYVFUdc6Q/7e49tprC0Q3FWXs3r17ws8799xzAVNUDcMwDMMwjG2UrCuq6mYkzzjwVvpaOapKzk/16tVdtyqh/KJcV4uryj9e955Moqr4HXfcMeZxVbcW5fUZBrQ9P/vsM0qWjL8WUhVzoufDiNT9unXrusfyTbVKlw4dOsR9PBn/xmwiVSq6M5Vy/oQ6OUmhi86rVeebfDiuVJXbs2dPwFMzhCJTQeVeFofq1atz0UUXAZ56ppoG5aEecsghQHLRj8LU1qB56qmnALj55psBz69S1zpdM8uWLcudd94JeE4jM2bMyOVQk+aII45wPqPKJy2qqn/evHku6jFlyhTAc5sRytUuzD81F/Tu3btQL+N4VKxYscB5M9PdGlNF6qicGES8KMM555wDFJ5bnKvzf9ZvVI866iggUkwlZHqrFpWJuPTSS53BsWR3JfZuLejGdMCAAYB3MyT7ER3sq1evBqBv374uBJYPqODKjwycgw75J4PCNrNnz455fMSIEXGLXbYmdPwlKnhMprVhLvjuu+8SjkW2OP6WgADDhw8Hitc6NRe0bNnSLfbr1asX85wWhrrR81uIhRmdH4YOHeosmPytYEWu072yjdpva3upAGzevHlApChJgozCsplui1tcVIg5ffr0Iq3q1EpcaWBXXHGFS6mLbqISjYqngi5WXr9+vbPIqlmzZsxzslr0pyk+/PDDznpMqRodO3bM9lDjolbN8dJK/OimNZkmQC+99FLxBpYk+SNnGYZhGIZhGNsUWVNUtZKINrZVOMbfOtWPiiJUqAER+wSINUEOAr89j///ySC1tEmTJi4MJKTsaJ5SorXybN68ed4oqq1atUpoP6UVsiw/woiMxx988EHAUxe14uzbt28g48ol2tcUPveTrXbHmUQRHP++1qdPn4RKTlhQAdjUqVMLFAaJM888E/DCp/mEGhucfPLJ7nh67733ghxSzlDa0JIlSwDPokw2XWvWrHF2W2GJXAjtl7IgjKemqgHH2LFjAU811XzBaySTKLyswsCgeeCBB1yDFBVIiYULFwKe7Vg81Db2448/ztIIC0dpC7r38LPHHnu4ban5qSg6DJiiahiGYRiGYYSSrN0yX3PNNUBsa7vBgwcDRScUy/Q6ujGATK2Dxp8gnkwbOD8y1B08eLDL0VTejnLm1Dpx8uTJQOJ2l2GkTJkyQCQvxm+krzwz2XqFEa0+Bw0aBHh51hq71IQwouNNx45seurUqeMS6aV+SOFYsGAB4Kk22ufGjRvn9ju/GimLoGeeeSY7E8kgGruOnbAUaCSD8vrWrFmTMAdQqrcMx7/++msgopK0a9cOCJ8ip8IhjW/ChAlOedtaUURGx6YKpXS985/b16xZwxdffJHDESZGERXle48YMSLha5VXqvNnopbF++yzj4sCKDKp4lpdH4Leb3v16uX+JlIYC1NSdT2/9957Mz+4DBL2KK0pqoZhGIZhGEYoyZqiGq2kCuWZJkI5WPEq+2VQ+8ADDwCwfPny4g4x56jCX/kqf/31l7OY8eeXqWWlFK18QCvOW2+9FfBW31CwMnnWrFk5Hl3yaPUrew7lDavyPYxWRlIvWrVqBXir/HgKvB5r0qQJUNAAPzqHXGqkfgNVKPttZMKErFcSKXRhqSROBuX51ahRw7U7bNGiRcxftSzWOTe6Klk5flLmbrnlFiD4fFb99qr0P+ecc2jatCng5fxJ2VfOqtQ1vSefqF27tjv/6xyo/EwpjnJCUVvggw46yCnPQTcWkZovhdB/Xlm7dq2L0PTp0yepz3ziiSdcm1V9npRUtZdVRX1QqJlGuvmaqmdIZMGZK3S/pP3Ib/sZjVw37rnnHgCuvPJKAK677roCr1UNUps2bTI32DiYomoYhmEYhmGEkqwpqso5ia6Kf/LJJ4HEq3nd5StnJxpVYI8ZMwbIvdKoVbxWfPvuuy/gqRdlypQpcqUvtUssW7aswG8hX0G/G4AISxVkNFptPvTQQ0D8lmtScoI2iC8M5Q5LSRUTJ04E4Omnn875mBKhPDdFGtTCVOpappGSKjPvMJPIN1XqZNBqYjqsX7+eu+++G8D9FYpEaf8V7du3d+05ZSIvM32dZ/Sb5Bqppj169ADg9ttvd8b3ynH3qzSvvvoq4HlCKn83zJx66qlApMWk5uV3b5BS9dhjjwFeU5tXX33V5bUGhXxShw4dGvd5RUmvueaapHOMlfepBg7RqAFHUNXx6aDtprGrscjRRx/tfFPlHBMUUlJVj1CYoipvVDUQKcxjWvun9u1sRTtMUTUMwzAMwzBCSdYUVbV604qydOnSHHDAAQBcf/31cd8j9TU6/0W5HepMFdTKRDlVb731FuApqsphKVu2bJGrCVVgi+i5SOFQ/qp8aPVbSEWJ9qUNGuURyf9QOY9i7dq1LsfoiSeeyO3gUqRdu3bcfvvtgKd4vP3224CXUxYWGjRowI033gh428DPhg0bgNiqfLWtVW5jKsTLOQ8birpIEfdXEp9++ulA8JXEmUbdinTuEEOGDHH7h3KYpWL1798f8NTnTZs25WSsfrR/Tps2ze1jcoVRZ5xLL70U8Lr6fP755wAcd9xxgSnCiVAXO+VyyiFky5YtTpmSw4v2Rz/RnaminW+CQJ7m/jx2jVHV/5pTYWj7SXmNlz8vxT8sKBc/2qVG0SXth7o30HzkF79ixQp3TVTXqqBzbuXPnsz2SgZFv9UqdsKECRn5XD+mqBqGYRiGYRihJGuKqvI2pGpEV/Kr29Luu+9e6Gf8+++/DBgwAPAq0YJG41A3GKlv06ZNo1mzZkBidwO/wlOtWjUmTZoEeCsSoc9Q7uH48eMzNod0UfcQv3uBv9uFxt65c2emT5+ewxGmjrbfnXfe6f49d+5cwKvyz4TaJB/CTp06uXwhrchT5e677y6gcPhRNyb1mO7QoYPLV0yElEblEffu3dupXFJWwoyOIX8lsfbBwnKttlaUNyeVUpEu5TbLoSNopWfDhg0uCiCUp6iIhvZl5XROmDChUA/LIFB3Hx2f2gc///xzFwVJNkd6+vTp9OzZE4BHH30000NNCjmA+Dswyuc7GWVOucfK8df1b/PmzaxevRrw1OWwdYrT/FJRIBXhePjhh12+aqKOUEZyZL1Hlg6w6ANNITolasuKyW+K+95774XmBlUsXboU8C7mSuxv0KABL774IgCnnXYaAH/88UfMe/W8CggUfgPvhKYCAd2gBl18pCTpfffd15kCN2jQAEjcfEBpCnXq1HEXwmTJ9YVHF+zatWu7NBPNszgXbzU6UJqLbu633357VxSSLk2bNi2y9ay+t7DXaUGh8KvCwzLE79atmws9ptMqOJe0a9eOm266KeYxHUvJWKfonCT0G2ztqE11spZCQaIbvXr16gHQunVrdxMkG6CgqVWrVsz/tTjq2bMn77zzTkqf9eWXX7rCo7p16wK5KzLS8aDzv871SrVQ69vC0L6l5j/6jOgGHEohCNsNaibQtSVf0YIiUevm2bNnu8XHG2+8kd2xZPXTDcMwDMMwDCNNsq6oxkNqhf76Df4lnavlahhRiEfJxPvuuy/HHXcc4IWOtYLeaaedAE9BjocKd6SkLlq0KPODToOBAwcCsWa/RbVx9StbyaAUiFwTXSilsKjSVlKhefPmgKemn3/++YC3GpXh8sCBA4vdFnHz5s1FbgN/21DwFH4VLCisl6gg5ZdffnHpOWFv3bv33nu740xKqt+eqjBmz54NeHYrKgqYP38+EHxkI12kxF1wwQVxn8/HxinRCk9R7biDRuNT++VUeOihh5zSnS3buUQoClG1atWYx1UI5W+Luueeezr1VWlHuh7quPRz3nnnBZ4apiLfRx55BIBXXnkl7c8qXbo04F1T9t57bxdFDaOtZFHo3K9Ihp+TTjrJbfN0rpmpYIqqYRiGYRiGEUoCUVT9dOvWLeb/Wn3K5iGMKLFf+TePP/64WznKakp//URbB6lN4MsvvwwE3x5QljDKHVI7ynRYsWIF33//PeDlQUa3hATP6sO/Qg8CKfxSyVXsVKFCBcBbYSovrlGjRs5yRa9RXo/yPx9//HHAKzxbtmxZscf57LPPFii+8yPVVgbPjz/+uGv9mmye26BBg1wOkhpbKMqhdo9Bo9zZJk2auH+rLfGSJUsAuPzyy4GCqvDrr7/O4sWLYz5H21GRDSmq+Uj9+vVdEw7llfv5+uuvczIWHedffvkl4BUqpoLypXOtLqaC2vPqOJdV2qeffurmrqjLihUrgEgr7Wg0z759+7rzZ64t1RI19tDjOpfIRrFHjx7unB7PZjIaqcRTpkwJvI3xMcccA3jnfEU9/fUlhSGFX9teBXDgKbVF1RSEEV2T1UhE534px7nEFFXDMAzDMAwjlJQoLPesRIkSOUlM04pSK2VVRSvvL1Ns2bIlpnQ5k/OrXLmyy+dUtaOf0aNHA54lTKZbAGZiflLO0qmiffPNNwGv5ejixYtdg4RMkI3tJ6Un3r4mxUP7Zfny5WOeL1GihFMNfv7555jP028wa9aspMeS7PyqVq3qlAwZtsu+TPuUxlPcfUw2NHLmkAtHopaKhZGN7Sf7uui8aKk9qriWAu4/161atYo1a9YAsNdeewFQqlSpmNeoPXAyZHJ+sgNTq8OXXnrJWfkUhXJzn3rqqQL77A8//AB4rai1PZPJ9SzO/LS/SE1TlbcU6zfffNM1ddG+Jjs1KVZqzazcx/nz53PWWWcB3jYvDtnYPy+88EIA7r///gLP6djy56zLOeDQQw9158+i7OiSIZX5+Sv0UyHafgo8izQ1QchWhX86209ja926NeDde0j1jraaVFTiyCOPBLwGBoo61q5dO+azX331VRf50nmmOGTz/qUw9FsoOhjd/OWXX34BPGtA3QOkg39+0ZiiahiGYRiGYYSSUCmqO+64IwCtWrUCileBF4+gViS5IhPzk5qh3LamTZsCkVWU8nL9foCqHtfqP1vG6tnYfloN33LLLTG5RfHQ6loV4D/99BMvvfQSAAsXLgQoVs5VGPdP5ctJaUynellkY35q4ag2m/FQHqZfHapcubLLSdV5cOXKlYDn2iA3gGTI5PykGkp9+uGHH1y+sb9SWjljyiVTLnV0+005Wsi7Wbm5qZCJ+cnYXbmOyuFMBrnByOd4wIABLnKQCbKxf2rb3Hjjja4aXB7LReVylihRwrnL+D3G0yGV+d18881ArNd3siivVudJfVa2G26ks/10nPtbs6vJS/S2kVKsv4lQvUnbtm3ZuHFjkeNOlqCuD4pa3XLLLQlfozqj4rRQNUXVMAzDMAzDyDtCpaiKaCUgk4RRscok2ZjfqaeeCkS2ifJRMq10J4ttv/wmG/Nr0qQJQNxuX1J0EnWIadKkiVNipfCpMlttWFMhk/Pbb7/9ABg2bBgQUWcSkUiZmzlzpmvXK29LqZLpkMn5yX9RLiPg5RIr/1pe0lLopE5lq2tYto+/Ro0aAd6c+/btq+8FvFx4qd8jR450UaxMuMGkMj9V8Kvdq7qBJWLlypUuT17epLluy5vO9lMOuqIu1atXT/l7de7RdlMXzkw7GuT6+qD8dkUwlIN7wAEHFHitKaqGYRiGYRjGNkmoFFWpB8ppyTSmWOU3Nr/8xuaXOlJ86tev7/I75Qur6lspqn///TfgnT9HjhzJf//9V9whOGz75TfpzE/KqjpVJWLatGk593r1U5ztd+CBBwKeO8Upp5wS8/yXX37p8tXl7CLF+JtvvgG8vNZsEfT+KbVZKvsRRxzhHCvkmpCOW5AoTFENxY1qrgh6Q2cbm19+Y/PLb2x++Y3NL7+x+eU3Fvo3DMMwDMMw8g67UTUMwzAMwzBCid2oGoZhGIZhGKHEblQNwzAMwzCMUGI3qoZhGIZhGEYoKbTq3zAMwzAMwzCCwhRVwzAMwzAMI5RsX9iTW7tPl80vv7D55Tc2v/zG5pff2Pzym21tftGYomoYhmEYhmGEErtRNQzDMAzDMEKJ3agahmEYhmEYocRuVA3DMAzDMIxQYjeqhmEYhmEYRiixG1XDMAzDMAwjlBRqT2Wkx1133QXAxo0bARgwYECQwzHSZMiQIQBce+21MY+XLBlZ33388ceceuqpAHz33Xe5HVwaHH300QCcf/75APTo0SPm+fnz5wPw1FNPATBnzhy++uqr3A3Q2OopVaoUAA0aNACgc+fOABx88ME0b94cgC+++AKA119/HYDNmzcDMGrUKACWLl2auwEbSVGyZEkOP/xwAM455xwA6tevD8A333wDQPfu3QFv+73yyivu/U8//TQACxYsAGDTpk05GHXyrFu3jjJlysQ89u677wLwySefxH3P9ttvz1577QXAgQceCEDjxo0B7LyaIqaoGoZhGIZhGKGk0Baq2TKUrVKlCgAnnngiAL/99hsAHTt2BODcc88FYOrUqVx11VUAfP3118X+3lwZ5r711luAt9I677zzsvE1BQjKEHjnnXcGPOVx4MCBQEQJeeKJJwD49ttvAXjuuecAWLRoUcrfk6v5SUm94oorAE8FivpejYclS5YAcPrppwPFU1azOb/jjjuOZ555BoDKlSvHfU30vACWL1/O8OHDAU/NKg7bmmG1za8gijbpvL5u3Tr3nCIVUq781ya9dujQoQBMmjSJzz77LNUhJCTX22/MmDEAXHDBBQDcdtttQPYicJmY3w477AB4ymDt2rUBOOWUUzj55JPjvsd/XimMhg0bArBw4cJUh5bV7XfVVVe5/S4dVqxYAcBpp50GwAcffJDyZ2xr55doTFE1DMMwDMMwQklOFdV99tkHgPHjxwMRlcf3fUDsyuuWW24B4NZbby329+dqRfLwww/H/H9rU1S32247wNsmzZo1A+DII4/U92o87j3//PMPAC1btgS8fMhUyPb8evfuDcB9992n74t5/tdffwVg9913L/D8p59+Cni/hV6bCtmYn/LEpk6dSrVq1Qp9bbztpqjA4sWLARg2bBgQbkU8KGx+RbPjjjsCXgTi+eefB2D9+vWUL18eiKhz0ei1HTp0iHn8/fffdzmuy5YtS3UoBcj19lu+fDmAOy7/++8/ADp16gTAlClTMvp9mZiflMBDDz006fekoqgqmpWOqpzN7VeuXDl3ztN9zPvvvw9QQNWfOXMmAG3atOHLL78EYPDgwYBXt5IO2ZifVPAZM2bQtm1bAKZPn570+x988EEALrzwQgDq1q0LpJdHboqqYRiGYRiGkXfktOpfK2O/kloY/fv3B7xVp/Ig16xZk+HRZQ6pT/5q8a2Fbt26AXDdddcl/R7lNt1zzz0AbvX2008/ZXh06VG5cmVXDe9H6uhJJ50EwIcffljgNXXq1AHgpZdeAqBFixYArFq1KuNjTYU999wToEg1NREHHXRQzN+JEycC6SmqmaBp06YAvPbaazGPn3DCCcydOzf3AwoRyj2WMgle/ri2n14jdeuII44AoEmTJgDUqFHDKUXKF8wkGzZsAODJJ58s8Nyff/4Z9zm5UFx//fUA9OvXD4iMXecT5f7lEzNmzAA8F47tt49cjhUFybSimgleeOEFAKpWrRrz+IIFC9x8JkyYkNRnXXvtte76LjT3sFGxYsWY4wq8efrz9ytUqABEKv0VEZZzRdiQcr1lyxaOPfZYIDVFVQ4P2Z6fKaqGYRiGYRhGKMmZonrssce6qsYXX3wRgDfeeAPw8lLE2LFjgUhFoXJhtOqUctWmTRsgnMrq999/D3jVq6oa//fffwMbUyaQD97IkSMLfZ088FatWsVuu+0GeHmtBx98MOA5PwStqCrfaNq0aW7fEnIqkFeqcpHk76jK12jq1asHwKOPPgp4+2lQ/PzzzwD88ssvLrdWyJ1Cqqt+i8LQMaw8rFzjV1KjH5d6IY4//ngA5s2bl/DzbrrppriPn3DCCQCBq7RSQKV8Rv+7ffv2gKeaVqpUCYBdd90ViKgk/vzARP9XTtmwYcOc00NY0Fh1nZADzJNPPumic9q3f/nllwBGmB6HHHJI3MflepNo3wwS1SUUp2ZE2ypevmZQkZqiqFGjhlNKxUcffRT3tX/88QcQzu0n5LAhn1fwFP4wYoqqYRiGYRiGEUpypqiWL1/e5bdIbZo1a1bMay655BLAq5r/999/XW6j1FepWMqTVIV2GKlYsSLgVcNLwcpH9t9/f+desNNOO8U8pxXkjTfeCMCPP/4IRDqSSM2ScnPWWWcB4Vk5q5LYr6aCl3/jr+qU4vT33387dSuRN2nQKN+wbdu2Bcb4zjvvAJ6iqtW1jqkaNWoU+Dz5Jsrz+Nlnn83CqAty8803F/maRAqG8lpTQZ8VtKJ60UUXAbHzT6SKSk1UpXE0ek7nUUUFFP1J1F0njCgPHLxzrDpdBaX0p4OOM3WM21aQh67+RqMOVWEjut7khx9+ALzzZz7Srl07IP3aBYBGjRq5+7NsY4qqYRiGYRiGEUpypqjusssuTkGdPXt2zHPqmDN69OgC71PenO78pR7or5EbTjzxRI455pi4z11++eVAwWrd8uXLOxW5UaNGgJdHpwpDVfruvffeQO4UEeXxScWPZurUqUDiHCQ9P3XqVJfXI0eAsCJlNR5SxD/++GMA/vrrr4SvVd51zZo1Mzi6olG+aa5IR4XNBlI+H3zwQdfJyF/Br2NGDhOrV6/O9TADY/369YCXi22El/333x+Arl27FnhO0biwKvvR5wPV2Gjfy0e0LcTs2bPd+T9Zateu7Zwqsk3OblSfeeYZ3nzzTcALWSlE1aVLl5jX7rLLLkAkJKI0AN2o6qL69ttvZ3/QaeIvEGrVqhWQ36H/6GIOIZP7yZMnxzwuE+E2bdq4VA1t08cffxzAtR7VdlUBVqNGjVwRUzbQAkfWL7pBjkah/ej2jvnOzjvv7CzBhEL+svvxt7CMx9q1a4HcX1AS3Tj6C6ii0c2t3hvvtYnSBYIO+T/wwAOAF/pfvHgx999/P+DZNW1raHEkk3Hw0nPSabkZNGFf3GYKhYe10Iou6lywYAHgpQOGzcapdOnSQOS6oRvTTLSTDooDDjgA8IrhVOA9cOBAJxoly+GHH+7+rRSibNkxWujfMAzDMAzDCCU5U1SPP/54V1Skdpq9evUCPAVH4WCpqNHhRamxMn0uLJQZNLLdkHqXq4TjbPL222/To0ePmMdkMaWkeFlRXXnllUDhLfNk4yRWrFgBRFqsyp4sm8jgPB4KtyaDPxVFqmRYKFeuHBBRiWUvlojCWh3KBk7bNhVT6Ewguyi/AppMkVUiElldRX9frpH1m5RUFUG1bNkyrba8+ULLli2dibzspxSxkT2c/urYHTx4cF4VT/lp3rx50EPICWq77E/fWb16tWvUEFZbsT59+gCR1r9SC8OanpAMssrUPYmihvHup2R7V6tWrZj/9+zZEyAmQqdjNlspOOG6qhqGYRiGYRjG/5MzRfX00093pu+///47gCvOUX6KEnyl6Kxbt87ZVVx99dVAfhQKyHqpMEUx35AheDQyQB40aFDKnycFVeqp9oGVK1emO8SkUMs+/7ZZu3ZtSkqq0Ofob9hyrJQjXJSaWhQqOovX+jIXKGc0E7mjUlLj5b0WlvOaC6RaSN1WPt97773nrMCWL18OeMrOyy+/nOthFhu1o7zzzjsBOO+88woo+jqWdN0Q7733HuC1fzTCyYgRIwC4+OKLgYLn3EsuuST0RXDR5wgdm5qXrNH8uf86Ry5ZssRZqUlxDBs6tmrWrMmyZctinhs4cCDg3XsVhvJVVfye6ToTU1QNwzAMwzCMUJIzRVX5i+DZqmg17Ud2VXfccUdojOHT4ZVXXgl6CBmjSpUqbvW7xx57JP2+J554AvCqAseNGwd41eO5yrtThbs/z1YsWbIk5XanO++8c4HmB0IVrkGjSvFk7NyUXytD66effjqp1XS+IHWkMCW1ODmvmUA5cXI3OfDAA4GIUtG3b1+gYC6x33hcrhW5asZQHD744AMgUn2sphtSUpWLqnnKCkcOJH369HEV2PkUvercuTOQuEnIF198kcvhZAzlPSonVUqqzivKh1SL2EmTJuV6iMVCtTSyYxSqSfnvv/8AuPDCC91zyu0/44wzAC+ao9fmGl1vv/vuO8Br6vLhhx+yYcOGmNeWLVs25v86xjT2UqVKucekzEp1zjSmqBqGYRiGYRihJOuKqpSsf/75p0hVR4b/8UzY8xE5FaiyUY4F+YRaLsqRoTC0elTuapiQyXS8tqDgVRinwogRI1wOqFCeq363oJEjw+jRo11OVSKkZGnVnSjikW9IQY1X5a+c16CVVKFIwzXXXFPgOVVNS2mUAqlcf+3b+v/kyZOdqhU2xwB5Nj700ENFvlaKnOoV2rdvD8A999zj8uqmTZuWjWFmBeUdqwLbj6JO+YbONb179wZia00A14I7n5RURQRbtGjhVEPlSN9zzz2A12Jbx9ill14KRFwqDjnkEMBrcqTraFDtVxUV1XF32223ARGfc3mdC52LFi9eDHj+sWq/esYZZ7iakoYNGwIUUGUzhSmqhmEYhmEYRijJmqIqj01VhPfo0aPIPCKtuD788EMAJkyYkLU79Fyg6lytoFXpmmoHiCDQ6kkrpcK2nVaY559/fvYHliZSoRKp+r169XLdsu67775CP+vQQw8FItWe/s8Lyn8zEeoatmrVKudTqRwy5Y372+DJ43bChAlbIKF7ngAAIABJREFURfecRN2nIPgq/1SYN29ezF8htxR53ErRat++vcuTj+7mlG9I6Zdzhf7fsWNH1+lOVcf+yuV8Ql2C8rEjXo8ePZyi6idRTmqPHj3cuefMM88EvPoH5Ven4yiTSaSoLliwwHVP1HVdfvB+1A52yJAhLsJWv359wMvbDUpRFXPmzAEixxB44wNvP9Txpi6OO+64IxCbo/v5558DBbtxZhpTVA3DMAzDMIxQkjVFVVVuuis/6aST+Oabb2Jeo76zHTp0ALyKbFVM//PPP/zvf//L1hCzjlb+WiVKZQ6zonrUUUcBXtcwv4cheJ6qBx98MOD1Q1Zlfxjx+536mTlzplNn/Kg6V6trKaq77bab+7ywdyuJVuHUK16PNWrUKO57jj32WJcX6Vfx8gHlncar8odIfmomfFmD5quvvgK83H7lmnXt2jVhZXk+IqVRnXEaN27szq2PPfYYABdccAHg5Q3mEzqvvvDCCwGPpGj22msvwMvTPO2001x0SUqjlNSFCxcCMH78eCDiqQ4kdEwB7zwdtKIq0nFiWLt2rfNPlWJZ2JxziTpR6dwvtRS8395/n6JjLTofNVfbJ2f2VO+++64rthE6wc6YMQPAtShTCGv06NHOviSe4XzYkcWMaNasGeDJ5WFChTZqX6ubz1tvvRWIWG0pJUPbR2FT3bipxWFQpvDF4b777ku4gNDjShxX68MtW7a4k/Jdd92Vg1Gmjk4uRxxxhDOmbtmyJeClpCSiTJkynHjiiUB+3qj6Wzb6yaewfzIcdNBBQKytU9iKqHShLk54W0UeQ4cOdcedLrgqXL3jjjuKM0wjASoAU7Gfbjqj0XVc55uJEycC+WUhlgn2228/J8IJpWKFBaVWJpNiKfFKPPnkk2k1yUkHC/0bhmEYhmEYoSRniuq8efM4+uijAc8g18+1114LeCGDihUrZs1ANhfMmjUL8JRjWVPIhitMKGwTHQIATwWWsgpeaEoJ8EoBUDFVGBVVJecnKg7q0KGDsxDxI6P1Ll26FHhOhUkTJkzIxDAzhgr3FBJt0aJFgKPJLYUZ+4OnpG4NYX/wihvU2lEMGjQodEVUKvRSC2WloaTD8OHDXYMEhaKN7HLRRRcBXlFQPKpVqwbAWWedVehnLVmyxEVK1eRH9klSzfMRhcbVPhW8Fqq5UiCzQadOnQL7blNUDcMwDMMwjFCSNUVVeSkjR44EIsnEsonp169foe+VupdM28cwo7wPzVu/iVocKsk8DEiB23fffWMel1VRjRo1XNs1KcRqKSfCvAqePn06AB999BHgzUv06tXL5eDIYFzFcH70/DfffOMKrMKCFPHnnnsOKDpPc2uiMGP/aMJi7h+NrH1UlDdz5syEr91nn30AzwZH+61yAKXahKWNbzSKKskIXY0xpLCmwk477RS32NPIPGpbm0pet2yOlLMqc3mdm9auXRs6Ky41W1DrYjFhwgQXlfPnfes+RedaFeWWK1fOXUOUy5vOfh4WKlWqBHjFcvfee2/OvtsUVcMwDMMwDCOUZE1RVcW7cpA6derkqsKV7+ivNBs6dCgAu+66KwDr168PtUqXLMrpVD6OKgHDpKiuXr0a8NRSmYiXK1cOgEWLFrntpfZrfleDk08+GfCsOBYtWpTlUSePVsFSmQoz9dcqOFGVqmzW2rZtGzobHLk1bEtKKkTU1ETG/spFDVszhmikVkih1zlQrF271ilT/jbAalShfM0wOzSo5kDnlxdffBGIWGvNnz8/qc9o06YNEKllUItumbBLrTMyg6zOtF/qehAP1Z4o11+ODEGb26eCXA3Uhlgcc8wxDB48GIDatWsD3jVFtRn+fPBNmzY5u7SPP/44e4POMrLVVHOGBQsWAF5r1VxgiqphGIZhGIYRSrKmqEqVkp/dXnvt5fKTlFsltfX+++8HYv3/IFIhmo/+qX7Wr18PwN133w147UknTpwYmvn9/vvvANx5552Al8cnxaJs2bKULVsWKDrH+IcffsjSKIuPFFVVCauiXyvpeCgnRxX+UhfCpqaCd9wpF1fbrFSpUm5bFoWiGEuXLnW+umEnXl5qPiip4oYbbgA8A3V/hW2DBg3cv5Ur/umnnwLFq5zPNcpTlCqlXNVXX33V5TQWhZSdTz/91Hk6S81K5CiTD9SsWRMgVE021ABmv/32i/u8ci7fffddd31TS+18ROqocsUVpdhjjz1cHYeu2Yow+v2ov/32WyByLX300UezPuZsc8UVVwBeNCQIddgUVcMwDMMwDCOUlCisW0SJEiUy1krioIMO4s033wQS57lIyZEf4ODBg5PqmJAsW7ZsibERyOT8kkGqnfKy5s+fn9HuOJmcn3w3pUbJ47YwlLsp71XljWWKbGw/5R1Fz09VnFo5K9cq216p2Zif8hl32WUXjjvuuJjnpCZLLVEFto6/TLdyzMb8pKTG80zNtWtI0OeXbJPJ+cl1Q4qT8toTfC+Au35Ed8vLJLnafspb1HlFUY///vsP8NT0KVOmZPR705mfuvAp71TRQSnjjzzyCFCwEj4IsrH91IK4Tp06tGvXDoCOHTsCXlROETdFj2+//XaApCMEyRLU+aV79+6At607d+4MwKRJkzL6Pf75RWOKqmEYhmEYhhFKcqaoAk7Rkf+WVtVSRbp16wZ4PdUzjSkeqSOfwgsuuIBatWoBcOSRRwIFKyOVeyulLtPY9stvMjk/5VDHq/RXFCDXnads++U3uZ6fvDrnzJkDeOrywIEDs/J9tv3ym6DmV716dcDLiZdvcy4V1ZzeqAaN7cj5jc0vv8nk/OKZ+yuNJihDf9t++Y3NL7+x+WUXLfwfeOABwEL/hmEYhmEYhpE9eyrDMIxs4S+emjt3bihboxqGYWwNxCtYzRWmqBqGYRiGYRihxHJUtyJsfvmNzS+/sfnlNza//Mbml99YjqphGIZhGIaRdxSqqBqGYRiGYRhGUJiiahiGYRiGYYQSu1E1DMMwDMMwQkmh9lRbe7KuzS+/sPnlNza//Mbml9/Y/PKbbW1+0ZiiahiGYRiGYYQSu1E1DMMwDMMwQondqBqGYRiGYRihJNQ3qjvttBOLFy9m8eLFbNmyhS1bttCrVy969eoV9NAMwzAMwzCMLBPqG1XDMAzDMAxj26XQqv+gue6666hbty4AP/30EwDz588PckiGYRiGYRhGjgjljWrVqlUB6NOnj3ts0aJFAKxYsSKQMRmJ0bb55ZdfAOjRowcAK1euDGpIhsHuu+8OQMmSkcDRySefDECdOnUA6N69O5UqVQLg9ddfB2DAgAGALYiDpF27dgA8//zzAJxxxhkATJkyBYBNmzYFMzDDMALBQv+GYRiGYRhGKAmVorrzzjsDcO+99wJQtmxZ99ykSZMA+Pvvv3M/sCh23HFHAM4880wAWrRoAUCXLl347LPPAG/lf+yxxwLw1ltvAfDvv/8CcNNNN+VuwMWkZs2aACxbtizhaz744APAU1IfeOABAE477bTsDs4wotC+evHFFwNw4YUXApGiTIAtWwr6Y+uxJk2aADBq1CgADj/88OwONgvouOvduzcAL7zwQl4eg/Xq1QNg8+bNADz99NMA3HrrrQDccsstwQzM2Gpp1KgRAHPnzgVg++0jt0Y6P3zwwQeMHTs25j3fffcdAC+99FKORrntYoqqYRiGYRiGEUpKxFMZ3JM5atG1xx57ANC/f3/AU0SiOf7444Hi5Y5logXZI488AnjqYSr8999/gJeDNXPmTHbZZRcgM0pxJlusnXfeeQDcfffdADRs2JAvvvgi7mv33ntvAF577TUAl/fXunXrjOb6bWst5Gx+RVO6dGkAhgwZQpcuXQCoWLFizGu+/PJLABYsWADA448/XuBzunXrBsCrr74KwKOPPpryWILeflKDGjduDMDUqVM5/fTTM/b5uZqfzifffPNNzOM6RzZs2BCAzz//PKPfm6v5lSgR+Zobb7wRgEGDBgFw8MEHA/DJJ59k42uzOr/y5ctz9tlnA3DffffFPKcccUUan3vuOQAmTJiQqa8H0pvfCSecAMDEiRMBL689GVavXg145xdxww03APD777+7xxRt3bhxY9Kf7yfo80u2sRaqhmEYhmEYRt4RihzVa6+9FoBLLrkk5vG1a9dyzDHHAPDxxx/nfFyZplSpUgA8++yzQETFadCgAQD9+vULbFzxqF69OuDlCSs3Nx7ff/89AGPGjAHgjjvuAOCoo47aKqqna9euDcABBxwAwPLlywGoUqWK26ZFoRyoww47zCkJ8ZS9bFGyZEmneDRv3hzwogKKVqjyPZoddtgBiIwb4NRTTwU81a5ly5YA/PPPP1kaeeFon+vSpQs//vgj4Ck1Dz74IABff/01AL/++mvCz5FzRWGvMXLDzz//DMB7770HeAqqok/NmjUDMq+o5orrr78e8HJudY7UeSUfufjii50yrCitcjdPOukkANq2bQt47hsnnXQSw4cPB2DhwoU5Ha8YMmQIkJqSKnbddVcAjjzyyJjHX3755QKvfeKJJwAvB/79999P+fuyyZgxY7jgggsAb3+cNm1azGvOP/98wPutbr/99gKfc8ghhwC46JY499xzAXjsscfSGp8pqoZhGIZhGEYoCVRRPeeccwC48sorAW8lJnWmZ8+eW4WS6qdMmTIAPPPMM07JCZuiKpRPlQxStESXLl0YNmxYpoeUdZQzrZyqAw88EPBW0GvXrgUi1eSp/D5CimYuFNXKlSsDEZVbq1qhqlX530o9l3J8xhlnuFW05i7kYCFFOShFtWvXrkDk3KG80oEDB6b8Oaakhgfl8Sn3T4qq6Ny5MwCjR4/O7cCKiY4hfwtwRas6duwIwKxZs4DYugUdx4p+vPvuu0D28lmTRcffwIEDWbduHeBFWaQaVqhQAfByNy+//HIgsh3lSqHH0skNzwZr1qwBIs4ZADVq1HDOIOmge52zzjoL8PJ4r7rqquIMs9jIo7hr167u/kvRP/1NhCKn8Sis9ikdTFE1DMMwDMMwQkkgiqpyWS666KKYx7WKUU6LcpTChFb5yuNQpaZWjakS1ryka665BkhtZSQ/VVG1alXXZcyvtoYRVacqV/roo4+O+zr5/abD999/XyAXOxvsueeegJdHpKr2aFRdnY4qIzcIqctBoW22efPmAvvftoIUcOXzby1IXZMalc9UrlzZVbvL71eoTmHcuHFJf97s2bMBL98zKOQqUapUKZdv+s4778S8RtGKt99+G/DUU4CnnnoKiJ8fnwt0HpNXr9D1Snn8lStXdseZVOBTTjkl5j277bYb4LnexEP+rPobFLpfueKKK4D4NSjqAvrhhx8m/JxatWoB3jnIz/Tp0wEvOpkupqgahmEYhmEYoSSnt/Xq1CSfVH/e24svvgiEU0kVqhLUX3XRatWqFfvtt19Kn7V27VqGDh2a2QEWE+XP6q9yD+UBWxhS5rSKat26tVuR6vcKM8ptlJ9vKsh/888//wS8fdnf0WvJkiX89ttvxRlmXKQs7r///oDXJ135tamwYcMG99d/jEpBDct+qwrxSpUqcfXVVwOeAiA3iq2d7bbbDghepck0ynnMZ5Tv/vrrrzv1SQqjoh3aTzt06AB4OZ7gdVXzR3HiVZbnkrp16wKeJ/js2bOLzA3v1KlTgcekwsqZI9fMmzcPgDfffBPw8jJV2a7z3IABA1wUVX91vhGKwOmap/z+MCElVep9vCjMPffcA8DDDz8MeB6wfkqWLOnyxP2K6l9//QXAnXfeCXjXxXTJ2Zlt1113dYnJ/ovf5MmTAc9kPp9QgvjcuXOdJYMO3qLYfvvtne1PWGycZBUmdPOZSnhY27N169auQCDsN6rt2rVLeIMqY2fZyTzzzDMFXqOCJBUZ5QqdeLTvjRw5MuZ5LTAWLVrkrJyK4ttvvwUiN73+ghUl3+s3CZpWrVoBkf1LIVRddC677DLAs57SvAwj2+gapxuhWrVqsWnTJsA7VufMmRPzHi0uo9E1s02bNoCXehZ0IdmTTz4JeELGwIEDE4oZCivL8jCdAtRsoUWD0g1VHKpFhUSzF1980TW0SYRSHsJSEBaNztsK9ftvUMeMGePsxLTPqYWxH6U4PPzwwwXudWbMmAF4N/g6FxcXC/0bhmEYhmEYoSTrimq5cuWAiKqhO3HxxhtvAN7dvkLnUinBW51k6s4808hCZPLkydSvXx9IXlEtXbq0swxSCkFQlC9fHsCZ/mrVm06IV4noJUqUcMVUYaV79+5ARJFT+FxIBVbxn0LiYULm4Sp+EwqJ33///QDcdtttKX/2iBEj3L9V6Bi2giWppSeffLJrhyjFW401VCghFUipKWFELWH9hXzNmzdn6tSpQEGzcBU9KESndI9XXnklq2M1EqPzuZqFbNq0yRU0+pXURFStWtU11hC6toQlLUJFXYUZ2CuVQRGPTFsXZYL169fH/NWx9NBDDwGpFY0qbS4eK1euBOCBBx5Ia5zpou/z34MpynbvvfcmHTVVcWO8+5w+ffoABVPeiospqoZhGIZhGEYoybqiKlVjr732cisprSqkAl144YUA3HLLLYBnbgzQvn17wMs3UyuysLHPPvu4JOpEaNWplSV4SelSIWV9IUPnXKFVYJUqVQBv1asioXQI48pZSD1VvpjmHY3sm/wKiHJ5pLQGyU8//QTA0qVLAU/h16o3nZVt69atgcgxK1QIEWZjfOWQ6RjS+URjV7HK2LFjgcj2C9t8ZG0T77hTC1G/eqVCBc1FiupXX32VtXHmghYtWgQ9hLTRdlRear9+/Zyinyzjx493ua4qfgmq6Eio2EhKcTLKoH87SrUsU6aMaz8adOMCP1JQ/Raa6aL8eKmQuW7/qyYSui788MMPgFfTkKhgKhpFR+MViS1YsACAP/74o/iDjYMpqoZhGIZhGEYoyZqieuKJJ8b83bJli1NS+/btC3iKhyru/IrrunXrnN2O7vznzp0LeHlZYeGRRx5xJutCyofmKWVOlYVdu3Z1K1LZmEgVUo7akiVLsjzyzCNj599++83lxGg+fjPooJCVj9qZxiOR0fgRRxwBwKhRo4ptu1FcZO+iv8VB85owYQIQyS/X8RaUIXc6yEFD23batGkANGvWDPAaOpxwwgkuHz7MeasAX3zxhXOdSJbjjz/eKSn5xA477AB414V8RAbnUsZTyfXXNa9BgwZOfVSL7Vy7ivhRtEWWaOkwePBgIBLR0Dln/PjxxR9ciNG9wZlnngmQ89bwivaq6YTOd6nsT3LwUZOjaEaNGgWYomoYhmEYhmFsY2RcUZVRrt/nbcOGDc4QWIpqw4YNY16jCsKzzjoLiKxClPtQsWJFAJo2bQoEn6uqFaWq9mvVquUq5aUaKjdHeYNC3nNr1651leR+o315emoFFhRaRU2cODHp1ZLMfmfPns3ZZ58NeLk5YVFUpd4rry86L7ooVJkdJj/ATKDcaTlAgLe9wlJlnA7yoFTuqtStOnXqOGcHKf9BKzvaH5VHKxeOatWqOe/DmTNnJvVZ6TR7CAMyt1eDGD+qxA4z2n6pICVVKniFChVc1X9YcqnTcT7p3Lkz4OX2P/bYY0A4cvxzhaKoiuD8H3tnHmjV+Lbh6wjNSYWiREqDqJQ5ofyQiGaSMSQ0GDMTpaSolCIRMhYpQ5klSlIRKpFZGjQQmcr5/tjfvd599pn2vNY6Pdc/u/bZZ+/3PWvtNdzv/dzPhg0bAJfKkmmlXF7pZFqZqpJ/6NCheZ7/77//vEQVKbWZwhRVwzAMwzAMI5CkXVGV8lmnTp18P1N1vyr9hDoZqSWZFLnVq1d7flV1ilCVvN8ceOCBQN47Z/mJdAcZq6TG8vjjj3t3z/Ig6e5FmaxS+rJ1Ry2VUI/KA7zvvvu8tn2qNNddodqFxtKwYUPvfZSVq/fTdvUL3cEedthhQKRTh3zCc+fOzfPaYcOGAdCqVassjjB7dOjQAXDt7sS6deuynveXSdT5R/nNzzzzjLdCo7aBqsb1S/nXaot8tG3btgUiCQzy2iofVZW6Oq7GtlBNRtULAuo2Fou2jXzTJQ0pVjp3zpkzJ3DtxOVV13E9NpcTnCIur7v+r3xjqXs5OTkl8piq76laxUejtJkRI0YArhVutj2r8aBjjzzFWkn8+++/gcjKeLY6pJmiahiGYRiGYQSStCuqAwYMAPIrc2XLls3nSY3tTBVL7dq1vc5Wse8XRB577DEgsZ7iUkqlKktRlV9JfsFMK6p6f1W6y5u67777AhFFW1XU2gbyeSrjVkT/XK/R+xx88MGA/4qq0LYqaJtJbY3OvQX45JNPgGB2qkoG9X/Wd02cd955Xt5eSWL9+vVA5Lgjz5a8gFKQtZ/6hfpsK+e3d+/enHzyyUD+RBXlpeqYIYKcY1wUWl2K5cknnwTghx9+yOZwMo7qHE455RTAZXhefPHFvlf5F4b2LZ3va9eu7Z3PdTw56KCDANfhT10mVceSm5tbYAV52Ln22muBiK9fq8SFoVUspSkEicWLFwMu4z12lVzPZ4O0X6jqDx57kCzooCnZWOHIZcqUAfAKcPr06ePt1FoSe/bZZ9M95KRQIwIxa9Ysr51lGNFyjEKpY8Op999/fxo2bAi40OfCToS6UK1WrZrXolR888036Rt0Euy5555A0RfKClbX0qmWrsTw4cOB8F+oyl6iRxHbRKCksnHjRi+mRReqjRo1AiLRTgCzZ8/2Z3D/j6K23nvvPSpWrAiQr9WvQuH1Wu3jYUM3x9oGsQS1jXay1K9fH3BL/roolR0l26Hw8SBblGITmzRpAkQutnXBLWQT0NJxUUjECprVIRlkARwyZEixF6qySgYRtbSPHaMsRrvuumvWxmJL/4ZhGIZhGEYgSbui+v333wMFm6xjUcRU7FJyQaiV2aJFi1IdYlqQuijuvPNONm3alPT7KZJL/PLLL4C7O/ObFStWsGLFCgCmT58e1+9UrFjRM8tr6V+qSbbVOhnDpZLq7zt48GAg0upXESKycKhgTsjwrqKcsKI7ZC07SakTio/RdzlI6LiibSX1N1WkUup9FTofJFRkGovCxMOqpArFiMWiJX8pxmFHK4datVLB7MiRIwFYuHChPwOLA61syq7XrFkzIBI/qCY8anSjOKrYc5iapMybN8+LXJO9qiQoqiKsDStk8VOLe63YSPEfP3484Kwc2cAUVcMwDMMwDCOQpF1RVVGQ7rSSQQUcEyZMYOrUqYCLYgkqhx9+uOfb+e233+L6nYMOOsgrRIr1vD700ENA8FrFJsLmzZv58ssvAWfELizEO9NIvVDBkFQoKXRly5b1wpgVDC+kvp599tmAK3YIK/vvvz/gGlIIecqCGMStWDpFE+kuX2rbK6+84qkxX3/9dVzvueuuu3oqnoqXpGYFMS6mJNOgQYN8x0ChSLugFhYlikLe5Q3/9NNPARg0aJBvY0qUlStX5nnUeToeFG8UlILagtC2iS4w1blCRbZSHmNXpESNGjWK/RytUgaB4447DnANJ2Lb5Kr+SJFa2cQUVcMwDMMwDCOQpF1RlW9Boc2qpi0IVdyuXbsWcG245s+fD7hA+SCybNmyPP8fNmwYtWvXBlxck+JiFHUj35u8j4cccohX4SmkpAZR1UoG3WmfcMIJgGvcoLvQwnx36UaVtapAlS9aYennn3++d6csr7T2SymtiqUKOwqTj0UVvUHxRUejKBQ1l5CvT3Fq3bt3944XChqXMlAY1157rZdg8fHHHwOu1W82o1eMiG+zXLlyfg8jo8gbHhtF1LNnTyDY57tMMG7cOK9mQSkb2QqQL4yHH34YcDUNSh1KN6+//jrgorz8plKlSl5r5lglVauievQDU1QNwzAMwzCMQJJ2RVVVuLpDKqlI/b3pppu856TIySu3zz77AC4nNp50A1WcB1HVSoaXX345z//1N5F6ki1FVUq/2trVrFkTyJshqu2iQPj27dsDJacSVR7bTp065XlePmip+UFE2+Scc87J87xCxdu2bUvr1q0B6Nu3b57Hor53S5cuBdz32JTU4KDqcD2GHeUvS6UbPXo04ILVtzc+/vhjr9GK38H/ul7RCk1sO+JUkR9X1w1qjrBx48a0fk6iKDP8gQceKDTpROkvaj7kB6aoGoZhGIZhGIEk7Yrq9oJyQDt06ADAtGnTaNGiRcLvo9ald999N1By1DuhO0Z5VQtrl5tp1qxZA+CpbkqnkF/4jz/+8DyNAwcOBEpe5beqqpXjKC6++GIg/mr5IKGkjSVLlng5lLrzl3ouv5myGr/77jsgsk+OGDECcH7kkoDUZ1WTh4FffvnFU52UBztlyhQgmB2aEqVy5cpeC1yh1TN1Bdze2LRpE1u2bAGc51wrbom0IU8HWmVKRklV5X5BOeqvvvoq4LZ10I6xSpqIznHXCpvO2TfeeCOAt638wBRVwzAMwzAMI5DkFOWXzMnJKfyHISQ3Nzcn+v/pmJ8q5N59912vy4ZQb2pVFku9ka/u1ltv9SrJ0+FJzcT8gkQ656dORFI5Vq9e7XVU8YtMbr8GDRp4+5q6L+nOWckTykTMFLZ/ZgblNWo1RgkGhXV6Shbbfslz2mmnMW3aNMCdF+SLVIZvpgni9pPq36hRI8CteM2ePTvh90plfsr3fvvtt4H8yupHH33kdTWMRRX8me7kl87tV79+fcDNt3r16t7PzjzzTMBtG+WGayUqU8TOLxpb+k8RLdvoYtQIBzo5KCKtpHPjjTd6F6gLFiwA3AVqSSnc215RAVitWrV8HolRGLoxBtcqNVsXqEFG9ipFH/klFiiar3Tp0r58frZREdWuu+6a72dqjiN0Ie5nS1hb+jcMwzAMwzACiS39lyBsfuEmE/NT04kvvvgiX0FZttsS2/YLNza/cGPzCzeZmJ8anFx33XVevKYYPHgw4ApWVdyYKYpa+jdF1TAMwzAMwwgkpqiWIGx+4cbmF243ljnbAAAgAElEQVRsfuHG5hdubH7hxhRVwzAMwzAMI3QUqagahmEYhmEYhl+YomoYhmEYhmEEkiJzVEu6B8LmFy5sfuHG5hdubH7hxuYXbra3+UVjiqphGIZhGIYRSOxC1TAMwzAMwwgkdqFqGIZhGIZhBJIiParZply5cgD873//A6Bdu3a0a9cOgCOPPBKA7777zp/BGYZhGIZhJMguu+wCwNSpUwF48MEHgcx3eyopmKJqGIZhGIZhBJJAKKrHHnssAHfeeSeA13P233//5cUXXwTg999/92VshmEYJY0HHngAgIsuugiAtm3bAvDqq6/6NqaSTt26dQE488wz8zy/5557AlCvXj0AmjRpAsDtt9/Offfdl8URGummatWqAEyaNAmA1q1bA/Dmm2/6NaRQ4msL1ZYtWwIwbdo0AKpUqZLn55dccgkTJkxI2+dtb/EO6ZzfFVdcQYUKFfI8d/nllwOw++6753n++++/ByIH2okTJ6ZrCLb9MkTp0qUBGDhwIADXXnstAAsXLvS28fz581P+HNt+/nP88ccD8NprrwGg4/9ZZ50FwNNPP13o74ZhfqmQyfm1b9+e5557DoAdd9xRn1fs723YsAGIHEsBRo8enfQYbPtll3322cdb2j/44IPz/GzXXXcF4Lfffov7/bI9v5133hnIf35YtWoVELnZ/euvv4CIqJgqFk9lGIZhGIZhhA5fFNWLL74YgOHDhwNQvnx5wN3N33HHHQAsX748rZ8btDuudJPO+fXp0wdw22innXZKZjze7w8YMCDZoUS/n22/DHDzzTcDcNttt+lzNR7mzJkDwKmnngrA5s2bk/4c237+UbNmTQA+++wzACpVqgS44lQVq/7888+FvkfQ5nfqqacycuRIwI1/zZo1Sb9fJufXuHFjTxU9/fTT8/zshRdeANy2kNp2zjnneK/5448/ALfaMW7cuITH4Pf269mzJ+AKiebMmcNpp50GwK+//pry+2d7fhUrVgTggAMOACKrjgBt2rQBItc1UiOlOGrpf+7cuRpz3J+XrfmpqP2qq64CnJpfELIOPf744wD8/fffSX+uKaqGYRiGYRhG6MiqotqvXz/AKaa6ctcVe1FX7ukg23dc8nSeffbZAFx//fUA/PTTTwDcc889aY2nSMf8WrRoAcBbb70FkM+XGs0TTzwBwC+//JLn+U6dOgERFWfJkiUANG3aNNGh5MNvRSDTZGt+DRo0AOCRRx4B4KCDDgKgTJky+lyNx/sdqUAqbkyGTM9PXmkdV7799lvAKR5XXnklgBd5V7p0aU9Z1JyPO+44AGbPnp3w5wd5/xw7diwQ8f2Dm6+8cx9//HGx75Hp+UnRl7p27733Fvn6UaNGceGFFwJQv359AH788cekPz/T85PnT0qc0CrFP//8A0CpUqUAqFy5sqcYy0MsxeqII44A4ttuwu/98+WXXwbgxBNP1Oez//77A7By5cqU3z9b81Ntjc7dsTUaMWPQ2IDIPgtOGd+6dWvcn5vJ+ZUvX54aNWoA0KVLF8AVt8eDijFnzZqV9BhMUTUMwzAMwzBCR9biqSpWrOh5VHTHLP9ippXUbNO4cWPAeXHWrVsHuKq5Hj16ADBx4kSvmlqV8n7zxhtvAPmV1OXLl3t396pe1XaMvStUvIp8cUYwqFOnDuCiUapXrx7370olT0VRzSS1atXy7uY1r08++QSAY445BoAffvgBcHF4tWrV8lSD2KrcksJJJ50EQO/evfM8L2U1EUUu00jpj9e3V716dT744AMgNSU1W0gxXb9+fZGv27Ztm/e6V155BXCKqjyPaooTpO1XGKpB0UqHQu9XrlxJ5cqVfRtXomisDz30EOCOM7H7q1ZyunTpwsKFCwFXB3D++ecDMGjQIMClOvhNq1atvH0tiJiiahiGYRiGYQSSjCuq8nPcf//9nlfs0ksvBVzodElBSqoUR3lyY30bUlEXLVrkKVXF+bEyjSoW5dkTmsPjjz/Opk2binyPo446CnAVuEGkW7dugPNcKk9y48aNhf7OIYccArhGFHqtvDxhoHr16nz11VcF/kwKwPjx4wFXkVq5cmXPh6wK5PPOOy+zA02Q2rVrA/DKK694PkXRqlUrAL744gsAOnbsCLg0keXLl3v+3JKoqFasWNHzxUv1kcKj1Z6gsMsuu3jK97Bhw4p8rbb5KaecwrPPPpvxsflFhQoV6NWrV57nVP0ftO1XFPoeHn300XmeHzt2LHvvvTfg9ssgo/OfvLbKEP30008Bl96g+o6Czinad9XkISiKaqro2mfevHlAelIcojFF1TAMwzAMwwgkGVNUmzdvDuD5HsqXL+9V+8vjUZIYMmQIHTp0AOCCCy4A4P333y/wtfKs/vvvv4HxVn3zzTcA/PfffwDssEPkHubJJ58EKFZNBVfVqkeIJBsECd35SVnVY0EUVP0OsGDBggyNLv2ULVsWiGwHzUPzUqamquDl4YxGeY36O8nn+vXXX2dw1PHz+uuvA25cAEuXLgXw2k8W1t2ucuXK3upOSaRnz57eipaUOHUaCxodO3Zkjz32AGDt2rVFvlbqd9myZYtcCQkSe+21F3/++SdQvIqmVa1HH33UUyPlb73sssuA9CtWfpCbm+t1pQwTyhdNhNg6DnUn8xt1A9WqYUEokWHFihUANGrUCHDqMMDdd98NOEW1sGufZDFF1TAMwzAMwwgkab+sLywrdeDAgSWuuh/c3VXfvn09j9VHH31U5O8ceuihQMTjIuVZitBdd92VqaEWifw16jShzlRSIOPJlezfv3+e/3/22Wc8//zz6RxmyijzdebMmQC89NJLQKRjyO+//w7Ahx9+mOd3Vq9eDThV45ZbbsnKWNOBPJjRyrE8mkUpqeLGG28EXFV1UBQsHV/2228/IKLO3H///QDcdNNNQPF9tJs2beqpAlINivvuBoHjjz8ecAkdsehYcs0113jPaWUjdt/2G6n7OnZC4ZXsem3fvn2957TNg4qOif379/e6E0lZVY6xfODKRpVHt169ep4PUqkNjz32WJZGnj6kwMWS7s6TQUZZq8WtFmQLdT/Td+nWW2/N95ovv/wScOc7dQ7VSlU2V2fSFviv4F5JvpKUdeBXIYqfpDMwd5999gHcMvBtt93mhWoXx4wZM4DIRaDeRyfegnaYeEnn/KpVqwa4OBQ1KSgILS/q4k9xJB999JF3UZ4O0jE/tYLVknhxFzPRaBlOyxzp3qfTuf1k1tfFTP369b3les2jqHaZ4vDDDwfc9/rzzz8H3Nx10o2HdMxP9hpF3OjiZeTIkd5Fdbxj6tChg/c+KuxMxQqQ6cBxNSOYPHkyAKNHjwby39yqXfGQIUO8iK5mzZql/PmZmJ8iqZYuXeoVmaroJna5VPFAWjpftGiRd3yRZSkVMjE/XYzp/FgQKnLcbbfdgEhhGcCSJUu8gHhd1KZCtgP/tb0WLVoE4BVOyfqmgPl04XdDg4LQ+V0WSC2NK6ozEdI5P9lniipi0/FF4qMo6kJV1wLJLP1b4L9hGIZhGIYROtK29C+Tt5RUGW8VA5QsUnRq1aoFuKVp3Qlo6aeo5ctMoHF89913AEyaNKnY35HcrrDm//77z1O8hgwZkoFRJk9sW9SikFojJVWoECdIaPlNjyWVoUOHAk6xWr16NfXq1Uv4fRSorv003SpIopxxxhmAK3BTTM/tt9+ekLqr99L7DB48OI2jTD99+/b1lHwVYkh5Ewr3VytSCH6MUfSS/8SJE4H420pu2LAhLUpqJlFDibFjx+Y7Poq6devm+b+OvT169PAKHsOIWodLSRVB/66lk65duwLuOFxc9Fqmkcp95plnZuT9Fdmo6z+p56liiqphGIZhGIYRSFJWVOWFUySTUChuPD64WC6++GIg0nZMdwAqyhIqKLjwwgsBOPHEEwHnh8k08vmpyEjxLwXRokULwHnJFN/022+/eZ44mebDiMLghaJTFIRsZB+p3LHKY7LIF6j3S1S9TBfycMsrm0yBZtu2bQE49dRTvXkU5cH2k6ZNmwIRD7uUVK0mqbGIkJqh48u8efMKjebyG3ngtZL0xx9/eI1RpDyqcYg8q7GeuHiVVz9R8dPy5cu9eWk1TquEFStWBFzho/4248aNo3PnzgCsWbMme4NOE/I4Bl31zhQtWrTwVrZUiOu3Qq596+qrr87I+8vPKk+1KaqGYRiGYRhGiSZlRbVUqVJAfn+ion6S4eabbwYifjgpQYpKEFJa9doTTjgByJ6iqraM5557LhBRRxXh06RJEwCvZax8tlOmTAGcr3XWrFmFtrUMA/K5xFa0XnnllUA4on6SoXr16kBkH4ynEUI2OeWUUwC372k/TTX2TNX1fqMq6lSi7lQpvvPOO3vHj6Ci41lubi5btmwB3OqV1BmtLqkds5g+fXpgVUcp/jqO//XXX576KAVV55bCiG1NHWSiY8HefvvtPD+T11iNcLQdjzrqKC/eT2kXQYk3igcpqUUlC5VEtD0VfQgu/jEM50St2ESP329MUTUMwzAMwzACSdqq/mPvmlK5GpevasCAAV62npShxYsXAy5AV+Heqq4bO3YsmzdvTvqz42XQoEGAU46vvfZaypQpA+BlF+puWJ5bVXPK7xf0sOriuP766wHnsRLffvutD6PJHvKWde3aNXBV1WogIdRONFlPqfJmGzZsCCTnOQ8aSg5YtWpVWvIp04lyix9//PE8z+fk5HhV1LE+N63qqPWmMgz9rjAuimOPPTbP/8uUKeM9t2zZMgDmzp0LuH1Yfj9lIev5sCNPv7I1tXrRsWNHz6erWoboRIegojzYWHRefPHFF7M5nLhQLrTqLXSdoZx0jb0opKRq/91tt928NCI1bAgDmqvaUwcBU1QNwzAMwzCMQJL2FqpCqkUyngz5z/755x+vQnf48OEAXven2Cp7KUbbtm1LbsAJItUwukVecZx//vmAa8kZVn+quk+o/aTQHVhJrfZX5a32vcKUAz+RAiPUeSlZTj75ZAAOPPBAwO27YURVyPJUz549OzDzkXLdvXt3IL/fdNSoUflWqa644grAHWtVWfzwww9ndKzpQNtC36kPPvjAS3L4+++/gfxV/TovyKepjF8/qFq1KuAybdNRla8uefIg5+TkeN5U5ZSHQVFVhzgh5btjx46Aq9EIAvq7aoVUqxJStdevXw9E2ojOmTMHyL+ioWSOZ555BoA99tgDiKygtm7dGghO6+mwYoqqYRiGYRiGEUhSVlTlWVNuart27QCXFffUU08BRfeULQx5ksB5UtU7N9Z/pdfqbjyI6G+iDixhy06VB0e9xOVN/fjjjwGnBukutCCU8VinTp0Cf56bm+v5kYOGxhW0Sn9w20Ldp9555x3A3eUni9QCedDlrw4T2m+PO+44wM1FlbhBQKpoYZ7nX3/9Nd+qTbdu3QDYYYeI3qAqayU+NG/ePKnjbjaQAilltSi0cqNjhzJvU0mWSRXlUKrzovJOpfamkh8pZfXcc8/1lFt5KNV9LIiJB+qw1adPH8Dtl1JQg6SkKgf9jjvuANwxQtcxq1atAlzmslZywSWPvPbaa4BTimvWrAm483r//v29VQF1pQyDsioVWCvAQfDxp3yhqg0xcOBAwMVE6cSpE2b79u09c3wiF5MqLlDBgOJk1ABAnxtEg7ZQy1RFsii+KUxUrFiRyZMnA+5mROjAqiVxtdksXbq0F+WkJRbtF+3bty/wc3755RevME77TtDQAThIqKmETupqYZdK2HaHDh28fVXRSLLghAndQKkgbObMmQA88MADvo0pFjUuKYyCYrS0PKkLby1DyxLQuHFj78ImzOhvoyKqIBwX9L3QmGR3UoyiLl50c5tMTNjuu++e51gKUL9+fSCYF6qNGjUC3P4Y5KB/te7VBaosFq+88grgWmzrOuOQQw7xLDXaBmqLqvnqUdtqxIgR3rlC10V+X6hKWFTzjDFjxuR7jcRAiRRBIHhnXMMwDMMwDMMgjcVUWmJSaLNCqHV1/sYbb3hBxwofV4xUbJi/li8rVarEtddeC7hlOwVgy1QuqT7IqL2rYkgUeREmnnzyyXxKqtAyihoaKCJJamo0KnbT8p0KRBSIvWDBAt/bzBWHlALFVAUBBacLbYODDz4YSKwRhorlBg8e7EWuKS4pqEvJBbHXXnsBTjWQ4jF9+nQgWDYhNWrQErKKqaSabtiwwSuU036nn+k4KkuRonUUUxV29DeRyjVp0iQfRxNBKwxS5ITOXVJYn376aSBSjBNv8ayOm7NmzfLeT8ccv1oXx0OvXr0KfD5IKxdCCrfONToPxRZjS9Fu1qxZvjbuYuXKlYAroqpQoQIQUVYVn7lkyZJ0Dj9ptOo5b968Yl+rlswqcvQzitEUVcMwDMMwDCOQ5BTV3iwnJyfp3me6E9FVuGJuYt4fcH46obvInJwcTwVRe1KFIr/xxhsJjyk3NzdPH8hU5hcP1apVA5yCeu+99wLxFRAkQybmp9icr7/+2rtjjJetW7d6KrKUBfmUVWSXCNnefoWh7XfkkUd63tB0kMr8tJ3UIENxUip+6t27N2+++SaQv9hNLX5VGKJVDHCFAyoETEWly/b2U0MNKT2vvvoq4PzR6W4vmsn5lS1b1lNKdXxUMZEUx0wHdPv1/ZPPUwW1KkxJN4nMT997FVWddtppgPMnxvL111/H3QRH81ODB3ArUKms4mR6+2l1UyuIinPScUXngkyRyPy0aiS/ulaKYmPGNJfopjYqlrrnnnsAV5Alv7Ja/27dutVT3tNBOrefzhcq4iwq0k4Fgory00qVCv0KQv7WeBoliNj5RWOKqmEYhmEYhhFIMhb4r+oyVdM1bdrUu3qXOqNwXSkEUlbfffddIOKr012oPB5FRR8FDfkE5dPVvMLEs88+C5CQmioV7vbbb/eU1JKEgtUVlxME5F3T96Vx48aA864+9dRTXsWpvH5arYgNLxd//PGH1yY3TH5HteKUkqrKW3nj062kZoMHH3zQO04KqXlBanWYTnTMkZIaJNTIRuc0KUiK7tN5LzqOr2/fvgl/juLltO8GGa2Q6lHn7Ewrqcnw3nvvAW47zZgxA3CKeGyiBuCtSGllVGqs0HkhDOh8EX1+LkxV1fcvnu+hIvN0DZAuTFE1DMMwDMMwAknGFFUh9eKjjz7y7kKlBJR0lAc6e/ZsIFytRVVhrPDfaGIVOaU2KPFBKsD333+f8XH6gbxYysYLEs899xzgcgIPPfRQIKIqyvtWkFpQ0HtMnjzZq5APE/ILxuY5qsmEvo9hQJW38qGC8/1PmDDBlzFlC/msVVkf5DQQNT0588wz8zzfu3dvIHI8Peuss+J6r2g/f0E5l0ElNk9U3s0go1qXwir6SzpSVtPVSlpe6nQnqpiiahiGYRiGYQSSjFX9B5FsVa3KW6XcPCkgV111VSY+ziMT89PdfdeuXb0KPmXkZtsvHJSqf1X8Pvzwwxx00EFpe99MzE8VtzfccIPnmY5VVJV/qxzOTHkeM7391O5QirD8gZqvFH+1Bkw3Qdk/M0W25zd+/HjAeY2lVmbK927bLzViq/5jPe+ZxrZf8pQqVcrr4KfjZCLoPFNYHm08WNW/YRiGYRiGETqye8uznaBKUPlekrlDCQqaix4Nx3777cd+++0HuO4kQeOFF17I81iSkR+3sDSGIHQ0MuJn//33B5zy/+KLL/o5HCNOpk6d6vcQjATZtm2b131Qj0HCLlQzwMCBAwEYNmwYkFjorREeypYtm6+FouEfasM7btw4wN1cKXhcxZxGOCiokNMILoW12DaMVLGlf8MwDMMwDCOQWDFVCcLmlx3UwOGTTz6hf//+ADzyyCMpv29Q5pcpbH7hxuYXbmx+4WZ7m180pqgahmEYhmEYgcQ8qoaRIN9++y0QiUFasGCBv4MxDMMwjBKMKaqGYRiGYRhGICnSo2oYhmEYhmEYfmGKqmEYhmEYhhFI7ELVMAzDMAzDCCRFFlOV9PgDm1+4sPmFG5tfuLH5hRubX7jZ3uYXjSmqhmEYhmEYRiCxC1XDMAzDMAwjkNiFqmEYhmEYhhFILPDfiJuTTjoJgClTpgCwZMkSAAYNGgTAzJkz/RmYYRiGYRglElNUDcMwDMMwjEBSZOB/pqvKmjRpAsCrr74KwJdffgnAF198AcAPP/zAxIkTAfjxxx9T/rztrWouU/Pr168fAHfffTcAO+20EwBvvvkmAGeffTYAP//8c1o/17ZfuAny/OrWrQvAtGnTAJg1axYA11xzTdzvEeT5pQObX7ix+YWb7W1+0fhyoVq6dGkApk+fDsAJJ5yQ5+ebNm0CoHLlynz11VeAuzhKZXnZ7w19ySWXADBu3DgAdt55Z/7999+0vX+259eiRQsArrvuOgA6deoE4M3p6KOPZv78+Wn7PL+3X1E0atQIgPfffx9w++tjjz0W93sEeX7pIMjzO/XUUwGYMWMGAIcccggAH330UdzvEeT5pQObX7jxe361atUC4OmnnwZg1KhRzJ07Fwi3EFWlShUAfvnlFwBuvfVWAO644460fo7f2y/TWDyVYRiGYRiGETp8UVT79OkDRO6oAL777jvAqVDvvfceELkz6dWrFwCrV68G4JhjjsnzO4ng1x1JzZo1AXj77bcBqFOnDgCnn346L774Yto+x6/5lSpVCoCHHnoIcEv/69ev91RkLammQhDvKMuWLQvA2rVrAVi3bh3gFNa//vor7vdK5/yaN28ORPYxgFatWrFs2bK4fnfkyJEALF++PNmPL5Agbr9KlSoBeH+bLVu2AG77JbLika35qZixY8eOgNvnTjzxRAA++eSTTHxsILdfcXTt2hWAp556CoDRo0czYMAAAP755588r/VrflqZ2m233QC8c55U/mgOO+wwIDGlX/i9/bTadOihhwKwww47eNvnueeeS/n9/ZqfrIvHH388AD169ADcPpcusj2/Bg0aAO64onOJrsFycnL44IMPAFi8eDEAkyZNAuDDDz9M+PNMUTUMwzAMwzBCR1YV1W7dugHw6KOPAi7eqHXr1gD8/vvv+X5Hdyfy+r311luAu3tJBL/uuObMmQPAkUcemef5W2+91Yt2Sgd+3zGL119/HYhso40bNwLu7kzKYzIEZX7R6C5TivGYMWMAt2qQCMnMr0OHDgDccMMNeZ4/+OCD9Z56rzz/jv1Z9P///PNPAO68806GDBmS8DwKI4jbT8cR7bNTp04FoEuXLgm/V6bnd/PNNwNw22236fPy/FweuerVq6fzYz2CuP0KQ3UQqgc455xzvJ/tsssuAPzxxx95fidb8+vdu3eesWlV7eSTTwbgv//+K/R3f/jhBwDOO+88AN599924P9ev7ffss88C7jul+f3000+eoiplLhWyPb+dd94ZcCsY+++/P+CuZ2bPnp3Wz8v0/Bo3bgy4AlKt2JQrVy7u99i8eTPgCuUTWfk2RdUwDMMwDMMIHVkL/O/cuTMPP/ww4GKLbrnlFqBgJVXsscceef6vO+UwsOuuuwKR9IKC+Oabb7I5nKwhlfioo47y/gbyPXbv3t23caWbAw44wFNSP/vsMwDP/5YJon2nSlioWrUqAN9//z3gvJYTJkwA4Pnnnwec2lYUUme1ijFo0CDPdywfZElD/qutW7cCMHToUD+HUyTa1oVRrVq1LI0kM1x//fWAiwp74IEHAFi0aBHgtlE8DB8+HMirpPpJ+fLlGThwIAAXXXQRAP379wcSU8Br164NwIEHHggkpqj6hRTU2Me5c+emRUn1C62Q1qtXz+eRpM7hhx/OSy+9BLjrFvH333/n+X9R12AVK1YE3Kq5Ep1i/eCJYoqqYRiGYRiGEUiypqi2bdvWq5B+7bXXABeqXRhVq1b1fK3yNvbs2TODo0wvBxxwAOAqiGORd6ekIW/Oyy+/TOfOnQFXHV+S6NGjh6fyyHemqvFMoErKpUuXMnjwYMD5n6WopoLUYb3Xaaed5nnD27Ztm/L7B41SpUp5FciqWl24cKGfQ9puqVChgrdK0LRpUwDOPfdcAPbaay8A1qxZE9f7ABxxxBEF/nzmzJn5FKJscPfdd3tKqlD6SzK0atUKgMmTJwPw66+/Jj+4DKHcVD3usENeXeyMM87I+piMvGgVZvr06fmU1BUrVgB45xrVXxSkqD7xxBOAa/4j7/Hee+8N4OXhJ4spqoZhGIZhGEYgyZqiumjRIs+Lc9VVVxX5Wt15jRkzxsuYu+KKKwB3lR8GVHkdi6oEi6ruDDPKVZVfBUhrhyq/Ofzww4HIfqz9Udm/mUR/12yhNICSSvv27T1lasSIET6PZvtmyJAhnpIqpKAmkmWrlIrY94r+eSJe13TRq1evuI/348ePB+Cll17ilFNOAVxXQ6GKbKV9BFFR1XFSqxaxHtWSgo6TsYpxGGjZsiWQ198+evRoILIiCnD77bcDec/nEMlJP//88wGXmqLv1l133QXAhg0b0jLO8P1lDcMwDMMwjO2CrCmqY8eOZezYsUW+RlWt8kJ069bN62bx+OOPZ3aAaUQdRuRbFLqTlOdj27Zt2R1YllCv4xNPPNGr6nzwwQf9HFJakM9WaRXz5s0r0T6r3NzctHQUyxbq/CYVKjYnM5YjjjjCu+PXd9LIDuXLlwdctmb090ipMPKsxqPKSEGVehf7XjoWJ9PRKRW0IliU2qYxqcORji8A9evXz/P7YVLtpKjGjl11JyUF5RmHUSkuaNVX3RSl2qsbmtBxtXXr1l7aTSwff/xxOoeZvQvVeJg5cybgvtzvv/++10Zu06ZNvo0rURQhogBgoTDxF154IetjyiRa+pDcr8BgcG3k0rUE4Cc68KqwaO7cud6JsCShJgY5OTmhucG44447OPbYYwFn9i/sQrVGjRpAJB5I8T5qTGFkFt3sXXDBBQDce++9+V4jG82XX35Z7PvpXKF23Ipwi30vCR6pxuTEi2FdndsAACAASURBVNpMKpLxv//+y3cho6JT/S0KKojUaz7//HPAFeiG4aJI8VuxYy2qyZCRXVQEdeONN3rPXXvttQW+Vt8dFUoVdpGaCcJze2YYhmEYhmFsV/iqqCoIf+LEiYC7O1aLwDFjxoRKSVU0ilrjxaJwcc17/fr12RlYhlG7tGglFSLbVYUBQWOfffYB4Ntvvy32tYqRkRKuNobaniWF2HasS5cu9XM4caE4lG7dunkKnFrAFoZCqHfaaScvmNrIDmqIcdNNN+X72X333QfAddddB+QPGo9lt91284o4Ygs9zjrrLMB9Z7MVSaW2mmqjqXieaLT/ffrpp0DRzThUeKtWlFJU1YAjkUKzbBNbZKT/l/QizTAhFT+6cK8whg0bBhQfK5oJTFE1DMMwDMMwAokviqqM71Iz1A5OLfMUhxA21ApWfqJYo7Lu7kuKkiovoxRxceWVVwIF+8+CwqpVq+J+7aWXXgpApUqVADev4op1woJ8fVK/pRgfd9xxvo0pXtR2s169evTt2xcoXlFVA44VK1bw9NNPZ3aAaUSev379+gHh8CkKFU/p2FAQUkHlgSsMff+uuuqqfEqqvtcLFiwAsqekCimoWpUoCKlY8bQ11mqVVoCEiv/S0egj3ShKMrbISMpqSfGoNm7c2O8hpIyOlWeddZbXfnvPPfcs8LWKq/IDU1QNwzAMwzCMQJJVRVUtuhRTpSt3tZaLVebChuK1TjrpJJ9HkhlUsSsV6+abbwbcHXP79u0B1yI3yMRT/avqfrVylEozcuRIIPzKgCo9pURq/33yySeB+BQfv4htHvLPP/8wZcqUIn9HHvhevXoBEa9VkD1+haHvW2H7n76Xd9xxR9bGVBxHHXUUAOXKlSv0NVWqVInrvZQuEj1/NQfQd/brr79OapzZYMKECQA888wzgFtJLAg1pGjQoEHmB5YmlI4S61H96aef8jyGHW0bzVPnlDCutP3+++/FNsLQCrhW2lavXp3xcQlTVA3DMAzDMIxAklVFVd7TI444AnAVoGFXUoUqkBX4H4sy9cJI9erVvQpbeXM2b94MuGr/F1980Z/BZQhV56q9nDLnwpREIWrXrs2HH34IuP1TitSWLVsA503t0aMHEPEt3XnnnYDzVy9cuDB7gy4AqRfKd5VveMqUKflSGE477TTA+SPlH9xll12ASOX0/fffn+f90h1UbUR45513ALzcWims0b7F2BbB2i+1khOr0EV7dOXVVH2A38QG80f/X4piUUqqiJ2zmgIU1zzHT3RcifWozp07F8BrAhN2Yuf51VdfAdlvKpEKOjZec8013iqVWL58OeDUfOXCKxs9mzUMpqgahmEYhmEYgSRriuqkSZO8NnlSUkeMGJGtjw8EQfb8FcZ+++0HwPPPP+8pqbojVmWrWleWFC6//HIALrvsMsB14Iju3hFUdPeru2FRrVo1z4OqDMazzz4byF+FLOWxQ4cO3jaWL1ktL/1qrfrQQw8BeB3rRJcuXbxuQFJuhFZwYivEL7/8cq8bXuzfK8wEyZsq5N9TvmibNm0At1rx559/5sscVdvsc845B3A5jsoqzc3N9dpQB60FbkGJDHpOOeHFsdtuu3nJKvrdMLTdjs1Ljc1RNYLDvvvuCzhfO+Ctoj333HOAU+/lPZY3t2XLll7nt0xjiqphGIZhGIYRSDKuqCpT7ZxzzvEUqbvvvjvTH+sLV199dYHP6y44TFXiUs6kapQuXdrzEqsX8IYNG/wZXIZQ1xcp/vIc6w4zDNWcypiUv0+q6cKFC9lxx/i+7lJWFy1axOjRowE8f6t8dVJhs+VZlWov1VTV+rfeeisQSWSQ/zE20UFqgTzySjmYOXOmN49s9YBPBwV5NMPGm2++Wexr5D9Wmoi+j2LdunXe/vjSSy+leYTpZ9CgQUDx2afykL/wwgsceuiheX72888/Z2ZwaaQwj2qQc7UTQR53KYxhRnUX0cjnLb++UjZiV9Hq1auXNUU1YxeqKkTRSX/YsGHe0qN23I4dOwKuUCMRdNDSF1kt5n788ccURp0asQZ68corrwDwxRdfZHM4SXHLLbcArsWhlmvGjBnjPffrr78W+R5awtt5551p1qwZ4ELltYz+1ltvAYkF72eadu3aAVCzZk3AXZxNnTrVtzElipZAZ8+eDeSPEksU2QTmzJkDuFD2Cy+8EMjehaqM/DpRd+rUCYivMENFVStXrgTcxXaYLk6jGThwIFBwG1IIZjxVMugmObZoQ+eP/v37exFPYUA3gIVFoinUX0W30RepM2bMAODiiy/O4AjTQ2FL/yWliKp06dIA7LXXXj6PJHl0TJQ4A+548fzzz+d5rdqM63gp6824ceM8e8Bvv/2W0fHa0r9hGIZhGIYRSNKuqKrgRlfaS5cuBSLLxLGtUWWOX7FiRZ7XFoWKe3T1rwItGX779OmT0vi3V9TWVgqcFOtx48YBMGrUKO9v37BhQ8AVQsjwLw477DDAxcqAi6dR8UqQlFSItCqUEqUg4549ewKFKyBBREsxeoxu5ThkyBDA2QISQcH6iq7KNooz0p17PLYTBfyrRbMiqMKqpIriilK0chU2FPg/fPhwoPD4m5NPPhnAi8sLOzovqlGMVqHAKalqUhFktBSu43/s0n9JI8zFYbLTaA5r1671rqFij49LliwBXPykVrN22mmnrP0NTFE1DMMwDMMwAknaFFUZ39U+U3dR8soNHTrU8z6ouEgFO/Itys8qhXX+/PmeOqC7aEUH1apVC3CetWjlyA922mknOnToUODPimvtGATU3rZMmTJ5nld72wsvvNC7eyquKEfbc8KECV44sBSwoMWrqKXjvffeS4UKFQB3t6l5hJErr7wSgJdffhmIfD+khHfu3Dnh95PS7ldBoHxSiaBCso0bNwIlp7GIvKlhKs4sjipVqjB58mTA1TcI+dm1IhCGOLyCAv8bNWoEuDavQkrqIYccAhTcyCAM0YaKhdN+qfPFmWee6duYMsHff/8NuAYpqmnQNZCK4eTvDxLaB7t3757n+ffff7/Y8fq5D5qiahiGYRiGYQSSlBVVKVJqo6k2XNOnTwegX79+QMTnIP/o+vXrAadY6Y5LnlXdkW3evNlT7+R3VOW8qtNHjRrlvdZP6tevny+wWiSjBmUbVW/Lk9q7d28gr3qqan8pxLrDik1tkKflzz//zOCI04PCxBs0aMCkSZMAWLx4sY8jSg/ansceeywQaV0pxV+KjZQdtWUsCLXYU3tS+Vv1vQsi8lvL16fjSklpj1pcPFWYvHM6Xzz99NO0bNmywNdIGV+zZk3WxpUqBW0bVewXVrmv39HjoEGD4orxCgqxntTo9rglCZ0H58+fDzhFVY9SLbWaHCQuuOACwFXua7WpqNqeunXrAtCtW7c8z69Zs4atW7dmYpj5MEXVMAzDMAzDCCQpK6qqUIxtL6mcLgUxX3TRRfnuiFW5r0cpPvKjgrt7kb81tj1iUCjMnxoWVE196aWX5nksqUj9Vu7oqlWrvKrHTZs2+TaudKPWoC1atODoo48G4LHHHgPg0UcfBZzqWlAbUWXJ1q9fH3AZnUFuOSpftdIbVEVeUpBiVZhStWzZsmwOJymknmol7uCDD/Z+ptU4eVK1AhcGlOet40oibZfVUESrhWPHjg1V4khhLVPDpPBvb8hXe/3113v7rlCCk9oeV65cOc/PR40albUmOKaoGoZhGIZhGIEkpyj/SE5OTokyl+Tm5ua5tUvn/CpVquT5PVRprXw8eeMy7efI5PyCQDrnd+655wLOk9u+fXveeOONVIaXMtnaflJ5rrvuOsD5UKXEqQtVhw4d2H333QHX/jHZDldg+2e6kPJfWLZmvK1yEyUd81P3t6effhrIm7UsRUf7ZbbTUtK5/Vq1agW4LlP77rtvsXmiSrRR57R0k+n9U63R+/fvD7hOVKpByXTXyGwfX5QXO3ToUMD5PeWpTrfamI75tWnTBoh0mgTX8S8RVMPRrl27tPrGY+cXjSmqhmEYhmEYRiAxRbUEYfMLN9meX4MGDQCX4ygfqqqSc3NzPeV52rRpQHJdrYRtv/TQpEkTwKU1qEPc1KlTAdetL92kMj9VGauK/Ygjjsj3GnWtO/XUU5MfZApkYvs1b94ciCRs3HrrrQW+Rp5UeXIzhX3/wk065ye/qdRg+fqLQt1GpfyvXbs22Y8vkKIUVbtQLUHY/MKNzS/c2PzCjc0v3Nj8wo0t/RuGYRiGYRihwy5UDcMwDMMwjEBiF6qGYRiGYRhGILELVcMwDMMwDCOQ2IWqYRiGYRiGEUiKrPo3DMMwDMMwDL8wRdUwDMMwDMMIJHahahiGYRiGYQSSIptCl/RAWZtfuLD5hRubX7ix+YUbm1+42d7mF40pqoZhGIZhGEYgsQtVwzAMwzAMI5AUufRvGMb2TfPmzQFYsGABADk5kdWZzp07A/Dcc8/5MzDDMAxju8AUVcMwDMMwDCOQmKJqGEaBXH755QwYMAAA5S3Pnz8fgHfeecevYRmGYRjbEUUG/pf0qjKbX7iw+WWH888/H4AHH3yQHXaILLr88ccfADRo0ACAVatWJfy+mZ5f+fLlAShdunSRr9u0aZPGQ7du3QC45JJLAHjyySeByNwTJSjbL1PY/MKNX/PbZ599AHjxxRcBaNSoEQCtW7dm9uzZafsc237hxqr+DcMwDMMwjNCRtaX/cuXK8cwzzwBwyimnAHDfffcB0Ldv37jfp3LlygB88MEHAHz88ccAnHHGGWkbayJoPGeccQY333wzANWrVwecCjVhwoQ8v7No0SIA3n777aSUKcPIJNqPd9hhB7Zs2QJA9+7dgeSU1Gyw4447eseEAw44oMjXPvHEEwBs3bqVc889N8/Ptm3bBiSnqBrpoXHjxoCzmQwfPhyAW2+91bcxGcnTqVMnABo2bAg4G1HDhg3TqqhmmvPOOw+A22+/HYBatWoBbj6xvPTSS96+rOPJ0KFDMzzKkokpqoZhGIZhGEYgyZqi2q5dO9q2bQvAf//9B0CzZs0Sfp/9998fgLp16wJOUc02F110EQBXXXVVnvGAu8OSZ65fv34Fvse6dev43//+B8Bnn32WsbFmGs1dSrJ8fz179mT9+vUAvPDCCwD06dPHhxEa8TBy5EgAateu7T03ZcoUIKIOBJlOnToVq6SKs846C4gchz755BMAmjRpAgRXMY6HatWqAdC7d28AOnTo4B1j9f3r0KGDP4NLAG3HMmXKAO5Ya4pqOLn44ovz/H/dunUAjB8/3o/hJMU555zjjffff/8FYPHixQAMGzYMcKu8hx56KBDx4m7evBmA9957L6vjLWmYomoYhmEYhmEEEl/jqZ599tmEf+emm27KwEjiR9XB99xzD+AqjKN9Ki+//DLg7hz1/wceeACAKlWqALD77rt7ilXLli0BPAUyyJx22mkAnHrqqYBTUKUgR/8t9txzTwAuvfRSAN566y0Apk2blp3BJsFuu+0GOF/mjTfeCDjFSqH3mzdvZvLkyQCMGDECgJUrV2Z1rKmgiv6zzz4biKgG0cyZM4devXplfVzJUL9+/YR/5/PPP+eII44A8B6lsIYJKaijRo0CIn5doe9i69atsz+wBJGSev/99/s8EiMdHHPMMYA7borBgwf7MZykGDJkCBBZhfnyyy8BuPbaawGYOXNmgb/z7bffZmVsQaJixYpA3mNPYfz666+AW1mPB1NUDcMwDMMwjEDii6L6+++/A/Duu+8m/Lvt2rUDnFKgCvpsIfVp5513Btxcbr/9di/V4OeffwZcBbF4//33ATj88MOBiKpYr149wCnFt9xyC4DnbQkKO+64I4MGDQLg6quvBpyy+NdffwFOQda2qVatmvca8cMPP2RlvInSuHFjrrnmGsCp28r/E5pXtAdZiqOSLFRF/vbbb2d8zMmiu155/8aMGZPn57rTHTJkiOfHCjrKd41G/nXlwopDDjkEiKx0aN8N8vYSXbt2BdwqhRTwAw88EIBSpUr5M7A0cfDBBwOw6667AvDRRx8BzvsXJLS/HX300QX+XJmhq1evTsvnadtfccUVABx22GGAOwbru/zQQw+l5fPSweWXXw7ALrvs4vNIEkdJE/379wciCUU678We10sKFSpUANy+rePn1q1b87xOtShdu3b1VlebNm0KuO9uUcyZMweAuXPnAnD99dcX+zumqBqGYRiGYRiBJOOKqnLERo4c6d39zZgxA0jMDyYPRKxCl4wqmwwnnHAC4NRQoU428igWxdq1awE3/44dO/L8888DLktWKoKyHoNC69atPcVRvP766wBcd911QP4EhjfffJNjjz0WcNXUmp/f1KlTB3Ce4379+nlKozw0uuPTNlIerhTHihUretXUqvTs3Lkz4NTzf/75J7MTSYIuXboA+ZVUMXbsWABeffXVrI0pVeSXjkY5xrHHmSD5UHV8LEyZU/Zk9+7dqVSpEhCfDywWee2Cyi677JIvY/Lee+8F4Kmnnir093Q8ln8wWx5/ba/CKtelxBW1Mvb4448DzvOv46v+DtHqlFRzJSFoVUf7eBBrGzp27AgUnjMaRLQiJjVYPv4VK1aUWCVV6HyglRqdI+Xf199Ear7+NtH89NNPgDvv6Roh+nugVfBvvvkm7rGZomoYhmEYhmEEkowpqlIKVPG+xx57eHdWd9xxR8Lvd+GFFwLu7uydd94B3J10punZs2eezxequE2G119/3VPrlG8or2rQFFVVvkcjP1RhWbbaB8DlOPpNq1atAHjttdcA2GmnnYBINp7uKKUorlixosj32rJlC+3btwdg6dKlgFNopQZ99dVX6Rx+SpQtWxZw2b+xyIsUpqpceYSVOBGNkjmCiFQLqWfyfSWCFG8pFFLonn76aQAOOugg77XqMBZUzj77bO9voO+dVp4K44ADDvA6G8kjLgUn0xTXCbEgz3Qs8kqLROotNG8lJAQ5RSVMfP/994Cr6JfaXRTKdldmsVZsli9fnokhZoQ2bdp4HcSEfNa777474NJwpJr+9ttv3srh1KlTAbeSKKU/XWTsQlUH0T322MN7TheoxV0AFIQikMS4ceMA/5Y8kplDLFu2bOGCCy4A3M6uiBYVValdm19cdtllQETu18lOFwWxNgyhooiKFSvy4YcfAv6HdVetWhVw+40uUHWRfdNNNxUaN1IUuvDVMt2nn34KwIYNG1IbcAZQMVxsow1tV504VRQXBrQdCyK2CCBIaOlYJ8bCLlR//PFHIHJh8vXXXwPwyCOPALBmzRoA/vzzT8BdHEVfoG7cuBGASZMmpXP4aUPbL/r4rhN8cSe7008/PSkbRDo4+eSTAahZsybg2g4XxhlnnFHkvhovEmhOPPFEINj7eBhRUaX+zrNmzfJ+VqNGDQCOOuoowAk1EgC0fXXurlGjhnezFVQbldrAjh492juv60JUNj2JLbpw/e677wBnkcsGtvRvGIZhGIZhBJK03442b94ccAqBlspffvllT9FJBCmNinFSHFS2lvwLQxExqSKTsQLxZZWQGV/LeOlQcJNBd1O9e/f2liulIsbaIBTn9Oijj3rPKU7Fb4VR8RlSnf7++2/A/d3nz58f93tp27ds2ZLRo0cDrq2eFGi/5xtLrVq18gX6qyjsrrvuAvIvVbVr184rlFNjCyGF4O677wYiy0BBQ2pXo0aNANeMQdveTxQnpCJNWYuEWio//PDDgFM5iiK2VfOff/7pLQkHcfuAK9A48sgjvefitVP5GXukfUj71HnnnVfk68ePHx+3+isbQ3Txqr5v+pwwKKkqtkkk2D0oqEmBCok2b97stf/WOUSruSqolmVMtpCTTz7Zi7NUsacsG36jgiitzjRo0IA33ngDcMeiIEVJmqJqGIZhGIZhBJK0KapSrNQiU/5FKQOXXHJJUvEOet/KlSsDThmQLytbqDhG8UOKy1KbuFTvlGRClg+kR48egCuqUgh9ttWgaG+N7iSlqMqbo0cpAFKwOnfuHJiw7rZt2+b5v5TQgpRU7bv77bcf4La5vEm6G5XvFZzSoe0YFOSbevjhh73WveKXX34BnHdVPlsVmjVr1qxQH7JWTtT4YsCAAWkeeeqo0CS2AFOPjzzyiO+qgf7WekwGrTqdeeaZeZ7fsGGDV4QaVORnz8nJ8YrB1DClOGrUqFHo/hk0EjkOqi1zNFphU8RhkNHxX0pqbJSWPI5BRh5gheB37NjRW9XUqpk8qrFNUeRXnjx5MsuWLQOcMhsURfWss84C3Dl92bJlXr2MfPFCqx7afmoRe/rpp/Pcc88BmY8JNUXVMAzDMAzDCCQpK6pSFhWrpMoxKX+6E4z3Ljn2vdU6TndlCn/OtmdTnlh9rjyzUgTSdackj2r37t0BV6GtRz8VSlXFywsnf2KbNm0AF5Y8ZcoUAF555ZVsDzFupApLEW/WrJkX2i9vlYLxharjVQ257777elWT8vcEDXn/Wrdu7T2n+BTNT8qwIsS0T4Obs6pXlUqhAHLFqgVRUY31UKv5hB4vuOACrxpezRzi8YIGDfnA1RAgDEgFPv3004HItlLETby0adMmVGHyxaEVqb322st7Tis/8sKGgYsvvrjA59X0RauGQSb2eLZ48WKvfXu8bXFXrFjhxU8q5eO+++4D0h/flCj77rtvnv/XqVOHJUuWFPhaHVdiA/5zcnI8tVWRhyNHjkz3UCOfnZF3NQzDMAzDMIwUSVlRVTaq7oyFKuSUS5bse8e+bzLNAtKBKvwyXc2tKv/bbrsNgLp162b08xJB3j7lpykAWJWNqjA+//zzgWBUVwv9XaXwy2cqT3U08lYpPFzV8GoIoCSEwYMHe1Xx8nnGVm/7hVY2ottPSn1SoL9UrWeeeSbP70jxuO6667ysziZNmgDkU72SyZ7NFvIvat6x/9977729zEM1blB1bhiUVSkd0bmp0UycODGbw0kI+fzKlSsHRPJe5eeT572wOgTVLZQvX977rgaxVXG8yBuuJAgpV3/88YdXSV7S23cGDXn7VWPTr1+/uJVUkZuby7PPPgu4ZAAlAvj93dQxXipx6dKlvWQX+eWVSCSlVee96dOnA5H6GTVEUD1HpjBF1TAMwzAMwwgkKSuq6tYQiyriCkK+wGgvXEE/l/cRnIcsGa9rJlFVrVIB0oW8qrHZiEFAd1SxFcVSp/z23xSEfKVSzKSElilTBogoMsqslLJTnB94woQJ3vtI+b/66qsB1xEo26hKVXfw0Z3hpK5+8cUXAMydOxdwSqq6JEl1/vbbb700g8IUAL/zjNVZbN68eZ6y+OCDDwKuelV/C+2v//vf/wDnUwan0qlzmRTWIKP8W1UUC61kLFiwIOtjiheluIhdd93V2y8///xzwHkaY1H2bG5urqf4B6WaOhnUlStWlZoyZUqgWjDHi1YuYnNUYz2OQUbn32HDhgGuC1yiSFGVd1Pd//xWVHWM1GMiKC/90EMP9ba1vLiZIjx7jmEYhmEYhrFdkbYc1dg8uxEjRuR7jRQNJQXEVmyq65Qey5Ur51Ud+93zXmieetRcMv15QUI+3djtpyQE+XqCiDyV6fZWSiFSdb3y5bKNKvnlPYpmyJAhAJ6vSEqqkHqvhIkpU6Z4KqUyV6VM33nnnUBiHb0ywXvvvQdE1FEpNrG5hkLe3KFDhwIRZU7KidDfrUWLFoCbbxCJrdwVGnPs3ILE9ddfDziFSXmOAI0bN87zWBg5OTmByy1OBGV13nPPPXmeX7duHeC6koUNnRdic1TD1KFKVezpQqtZscfcMCHfrvKO99xzT8/HmumV7pQvVFWkEhuQq+XD6IsZTUYXMlraUTGHlhF1IfTNN994B9ugLPlrPrGPmf68IKBl8sIKhm688UbAFVUZ2UORSw888ECBP581a5YXCH/DDTcU+JqCtpssN5dcckkaRpk5tm3bFnfBiV43Z86cQguugoxOdoWd9MaPH5/N4SSFjv0qalRRFTg7WbT1C+Drr78GXIvVNm3aeBF5YUTFqLHtiRcvXgwEr3mIkTyy44TxQlWtimXnVOOb3NxcL4ostv12urGlf8MwDMMwDCOQpKyoSoWRGqpg21gWL17sqaLFtVA77rjjUh1WxpD6G6sE1KxZM1/rsVTQMnqQkKq2446R3UZFAFomadiwIeCKVRTvZGSevffeG4BSpUoV+POTTjqJk046Ka73UjTTnXfe6e3vQUFxbRpjKq2U99hjj0CtWMSLCr+kbIQZLR3qEVzhigrlYpGtJjc3l7/++ivDI8wchanBQW6UkgpSkFXEmOm2m0GiZs2agGugEgbUCEbngOimMRCxUGlFPdOYomoYhmEYhmEEkrQVU8l3mo5CGsUcASxatCjl90sn8mkcf/zxgCu+WLRokadYpTJmxePofRcuXJjye6aDKlWqeN4w+W0U/r527VrANQRQC9V9993Xt5imbNC3b1/v34rk8quQTH42FRLJ+F4UUhPnzZsHuIiRUaNGAcEqfqhfvz7gIrXkiVIs0euvv15scxEVc3bu3BkouJmG2sv6HbtVFLGtfYVWrMzbGGzUKlWKlZAHN2irGImiAkcVZ+o4osYGKtAMg6Kq1d3Zs2cndTzUcUutpnV+9wttE9X+RMefVatWDXDNaxS5uPvuuwOuyF2FkGpGkQ1MUTUMwzAMwzACSdoU1XSiwP958+Z5gbtBQR4TtSBTtXXVqlU9r+aAAQMA4vKs6q5a0UGqqP/tt98A6NGjB+B/i8AaNWp4vtmBAwfm+ZnujLt37w64gPXWrVv7FtOUDdQqFlyTAIWWZ5uVK1cCTulXhb8C+8uUKeNVEytSSspNGJQNVZ5KlVHIvR4HDBjgrT4UhuKOlF4RjdoCSy3Q/4OEIpy6du1a4M8fffRRINKooSSjZIbNmzd7Kk8YkEdT30n5ybWyoTgqrVCFFa20xcZThQmdl7U6OG/ePC/errhGMNFolVUtb2KrXAAAIABJREFU2P1qAS9UZ9KxY0fAHfvLly/PWWedBbj9VGg1V0lOUsyziSmqhmEYhmEYRiAJlKJ6yimnAO4O7KWXXvJzOEWifEkpPUOHDvWq4NUaNlpxK4hjjz3W833utddegMsXVGuzFStWpHnkyaPtUlhbv08//TTP6xo3blwiFVV5d6KbPehu2290t6vHyy67zM/hpI0lS5YAzr+uu/sqVaoAEZVNIf3xsnXrVh555BHAVZK/9tpraRlvJlC+aPXq1Qv8ud8+9myh40uZMmUKVMeDihJiopsbgAtQ16paSUGNC+R9DBNSQOUHnzhxoueBHz58OOCuAdR6OpZSpUp554pXX30VyJtu4QdadVHDloJSmrTyJoVf+6dWef3AFFXDMAzDMAwjkARKUY3NDg1y5a247777gEiFe69evQA3D1XfFubRycnJ8X4mP6sUo8ceeyxzg06R2OzCsmXLAnD//ffneT6sLQCLo3379kBk+6m1qFVaZxbtc6pIHT16NAB9+vQBIp3xWrZsGdd7jRkzBoh4raWcGOHh888/B6BRo0ZepnMYiPUnat+TMlfSULdKqYk614XBEy+kojZv3tzLf1XnN61WTZo0CXDHFXHNNdd4q24333xzNoZbLHfddRfgEnqULrR69ep8SSpbt27N/gALwRRVwzAMwzAMI5AE6nZUHitVcoZBUVWm6JVXXund3atyvzhmz57t5ZnpriyoCs+PP/7IG2+8ATjPiqqPlUfZrFkzwFWt+u3HSTedOnUCXEeu3NxcT0UO6nYrqcgvJW9wUDzCfqD8VHnESzpXXnklAC+88IKXjRudaRxESpUq5XmMhbqqhalbUSIok7gwT3WY2LhxI9OnTwfc6pmO/fLL61EsW7bMq1MJSpKDVG0lFySSYOAnOUVFR+Tk5IQvV6IIcnNzc6L/b/NLjEqVKgHuouDSSy/N83M9r0KwdLaUBf+3n4LwL7/8ciDSCrhOnTppe3+/55dpbH6pofDxN998M8/z9957L+BaGWcK237J06VLl3ztJr/55hvAFWdmulmIbb9ws73NLxpb+jcMwzAMwzACSaCW/o1go+VWFbDocXsltvGBYWQSFXbssIPpCyWBdLYdN4ySjB3xDMMwDMMwjEBiHtUShM0v3Nj8wo3NL9xkcn516tTxinEaNWoEQOfOnQGYNm1auj6mSGz7hZvtbX7RmKJqGIZhGIZhBJIiFVXDMAzDMAzD8AtTVA3DMAzDMIxAYheqhmEYhmEYRiApMp6qpJt1bX7hwuYXbmx+4cbmF25sfuFme5tfNKaoGoZhGIZhGIHELlQNwzAMwzCMQGIXqoZhGIZhGEYgsRaqhmEYhpFhDj/8cACeffZZAGrVqgXAEUccAcAHH3zgz8AMI+CYomoYhmEYhmEEElNUA0K/fv0AGDZsGACDBw8G4Pbbb/dtTIZhGEbqdO3alWeeeSbPc926dQNMSTXCSZkyZahZsyYAr776KgCPPPIIAIMGDUrrZ9mFqs/stNNOALRt2xaAnXfeGYD27dsDcOeddwKwdetWH0aXGlWrVmXdunUA7LBD+MT7xo0bA1C/fn0ATjzxRAC+/PJLAO6++27vteeeey4An3zyCQAff/xx1sa5PVC1alXAHQAvueQSAArqrDd+/HgANm7cCMCIESMA2LBhQ8bHaRjRaHl/+PDh/PDDD0DkohXsAtUIJ507dwZg4MCBNGzYEHDH2vXr12fkM8N39WAYhmEYhmFsF+QUpEh4P0wgUPb0008HoHv37gDssssuAJxwwgkAzJgxg59++gmAUaNGAfDFF18kMeTkCVpgbuPGjZkwYQLgjPaxlC9fHoAtW7YU+35Bm9+JJ57IK6+8AkCpUqVSfr9sze/MM88EnDK3atUqAEaPHg3AvHnzgLyqaYsWLQC39NG6dWsAT1GOh3TOT/tNxYoVgYhir7tfccghhwBw7LHHAvDSSy8BsGzZMgAee+wxfvnlFwBWr16d7FA8UpmfjhmXX3550p+vY9Lvv/+e9HsURdC+f+nG5pc4Kpzq0qWLt9Sv57KNbb9w4/f8DjjgAMDtv3Xr1uXDDz8E3Dnzxx9/TPr9LfDfMAzDMAzDCB0pe1SlIMmjF4sU21NPPdV7Th4decemTZsGwPLly1MdTiiQKjRkyBAqVKjg82jiRz5B+U2LUwv333//jI8pnbRs2RJwSurSpUsBOPnkkwHnwymIzz//HHDKsTzHjz32WGYGWwy9e/cG4K677gLyejk1D3nmpAzvtddeABx99NEAXHnlld7v6Ps7c+bMTA47YaR2b9myhblz5wKwZMkSAPr06QNA7dq1AXjnnXcAuO666wB44403sjnUjKPvp7zVN954I23atAFg1qxZAFx//fWA+xsZmUHe1C5dugCRVRi/lFS/adq0KRDxNII7lnTq1Mk79xvBReeS+++/H3Dnkvfff987V2QaU1QNwzAMwzCMQJKyolq3bt0ify6ladOmTRx11FEAVKlSBXARTLF3Woo6KClIgZS6pYrlotTUd999F4C///47w6OLn/vuuw+AtWvXAtC/f/8CXydVsV27dtkZWJoYMmQI4O4Yr776aqBoJVX8+eefAIwbNw5wlZF+KarffvstAN999x0Ae++9NzfccAOA5xv+7LPPCvzdgw8+GIhsX81D/iS/FFWlXkhBlSLYs2dPoGAP7Ysvvgg4ReCKK64A4JhjjgHCr6jutttugFuZOvLIIwHYZ599vNd8//33gEusUPLB2WefndGx6Zgnr7TQdtT3RaknO+ywQ74Eh3/++QeIxOBEvzYRNm/enPDvpIPYY+PIkSN9GUe2qFChAgMGDACgUqVKABx33HGAW1lToo045phjAq+oNm/eHIBWrVp5KzNaqRHa1//7779C3ycdNRrZ5rTTTgNcbYbQiqMSibKBKaqGYRiGYRhGIEm56l93u2eccQYAixYtAuCbb74BnCK4bds2r1WcVJpTTjkFgDp16gDuDrpv374APPjgg4nMpViyXTWnO8jLLrsMgHvuuSfPz//66y/v76OKZCFfZCIKVibnd9JJJzFlyhQAJk6cCBSuqEpFWbBggZdBGvSq/z59+niqx0033QQ4hTURpN6NHTsWcErW66+/XuzvZmJ+WvHo1auX1zwiEZVJCqaq/w866KCkx5LK/Pbbb788/1+5cmXCny91uVy5ckDEy7lmzZqE36cwsnV80YrUCy+8ADgldf78+YDzo06bNs3LNdR39cILLwSgXr16QGLZssXNr0aNGt6/9913XyDiY4vmrbfeAqBHjx6AO28ccMAB3j4mZUorOPpeqrYhEXJyCi0kzkc6t190tT9Ewv399qhmcv+cMWOGdz4XOs48//zzgKtn0f9XrVqV0vEklnTMT7UFUg2VmiKVuCC0jxV1LaXj6KGHHgokl6KSreOLQvxffvllAA488EDAefsnT54MuDmlC6v6NwzDMAzDMEJHyh7Vv/76C4BJkyYV+9r33nsvz6NyG+VTkRIn/9QzzzzDr7/+muoQfUN+xQsuuCDP8//++y8QmafuQmNTE/R3DQpt27bN5zcrDN2FVqtWLe13XZnijDPO8BQ3Zdumgx139Lf521dffQXANddck/DvDh06lGrVqgFOYfCLZBTUWKQESBlo27ZtXMetoCC157bbbgOcf07Hy6lTpwLu+BKN/MkzZswA4vNdJ4pSF6I9srEoXzie44ISCpIhHftLKihrWYpqSefRRx/N1zZTar62hdIotKKhXPUgEdtJUTU24DzvmzZtAlwXQimqTZo0ASLHTXDnQXCJKmHwqj711FOAU1J17pAX3g9MUTUMwzAMwzACia9yj6pu5VmRMqB0gO7du3uqZBgoXbo0AGPGjAHyK6mqdD3nnHOAiKqhf4vFixcDMGfOnIyONV4qV64MRBRHeXAWLlxY5O8ozaBq1ao88cQTmR1gisjD2bRpU08RkFdue6VDhw5AXhU2kQ5bQSW2M1eYqFatGo8//jjgjjNSPqT0FKSkim3btgH5PaPpRMkQ7733nle7IHVVGdny4qsuQaxbt67QrjaqtpY3Nx7S6X1MhiCqhZnkueeeK/Y1WpHTvuFXIkNRfPTRR8D/tXfmgVaNbRv/HUOUIZS5SUJFlJAhEmWKqJTezGSq0EDRSBmi5FVIJBUyxVuIDJFkDGUuQyFTVIaKEs73x/6u9ey9zj7n7LPP2nutdbp//5zaZw/Pc9aw17qe675uVz8j1bQspDsO9T1YFk94PqlRo4ZXGyQfrRJrbr311tDGJcJdl/x/FHPQvn17wO3QV111lbeBf//993AGlyHHHHOMV6zSrFmzlN/pAk8X4vpiefzxx4uY/fUcxbiEjcZavXp1LzBdBRHFkWw81+ujioqf/vnnH6ZMmRLY+6pILt9tgsuDYtMGDBgAJPbbSZMmAcTqhlGorZ+Wm9XmuaSih6hyzz33eDeN/hv7qKA2z/vvv79XkKHoQcWIqVX0zTffnPLaqVOnFonBEbIAyPqgJeRtt922yHNVUFZSVFA+UCyYUTyZXNyGRTYXqCqaTndD5RekokajRo28OEYxbNgwIPxjCWzp3zAMwzAMw4gokVBUpTqp8EOm5Jo1a/LII48A7m46KiguZ8KECUCi7aRfHVUkke6mFEmhIPzkOA/dwYVdBCAuv/xywNkw1q9f7xXUlLaspTuz3377LaNYpjCRyX3SpEleMVV5kOqlqLXFixeX+z1zje6czznnHMDFDM2aNctTnKNOnTp1vLimHXbYAXCB+CqMEGvWrAHcEnqUkQJ5/PHH88orrwDFt6vWfKdPnw4kipv8Kkm+0DK+lFTx5ptvAokA9UxRTJwawajYMVlR1d9GqnnYxaiap2jWrFno8VRho1hKoQjLuKPzTnKbeEhYGxSaH3WSbV4650fpu8sUVcMwDMMwDCOSREJRFYqrkqIKLjg6bBQzJNVp5MiRQNGgfnCtUuWtkidOyo48gOB8XVIww1YC5C/1R8OMHDnSC/wvDkUZHXnkkUDC96koj6ihgi8p9cnbJBu0f0jRCavVaCZIydc2lmrn9242bdrUM9YrUi6qNGjQwItTKQ154IcNG1bET6YIq6VLlwY7wDKi41AFDZtssol3flRRptBxN2vWLAAaNmwIwIcffpiXseYa7YPysKrFbzJSbqN2vtE5s3fv3l7jguL2LR2HtWrVAlJrHeS9jbMqW1GjuqSk+s+fffr08ZT+qDJo0CAg0e5WdTFaBfXPp1q1aoBb2ahbt663yqqVb7XlVvFmUJiiahiGYRiGYUSSSCmqumJPRsqC2lrqrnT16tV5GZN8bJ9++ilQssKrZAJFXMjfKdQCUK1kAa+hQVTuvMaNGwc4n598t61bt/Y8Yf5KfkUXSQHYaaedgETlu5IQ9HeTH/Kzzz7L2Rwy4YILLgBcKHNysHM2KELnwAMPBOCDDz4o1/sFhdTDQYMGeV5pqYj+ZgTabw844AAg4bd95plnAFc5r+dEkeLaZcqTqsDq3r17A4mYHKnKlStXBuD6668HnPe8X79+gAsvzxcaT3KMk9ReoeNMIf6Kh4pjqkE6pDBqu/nTVOJAsnqq877a2UphrFmzZtrXvvHGG57XV8/VSp68x3FSWLWKpb/JJ598EuZwyo1WQf0oditKHs/iUPvlwsJCL/5O49Z3h77Ddd5MPr+oeYp+KuJx8ODBgY7TFFXDMAzDMAwjkoSqqEqtlLdMqk0yUg10VS8vkqrku3XrBmTWki8bpDpl4pWV+lualzMZKZcKxJ43bx7g8geVhJAvlMCQzq+olo0XXXRRyu/kbVF7OL22UqVKnvdTapcUurAVValPQt6abDnhhBNS/h+2WiCfsPIqte3A+aAVPi2V25+VOnz4cPr06QO4pIqoKqpr167lhx9+SHlszJgxgFPkFMStFqTJtG3bFnB/N2Ui6vhXGHZYec5PPfWUt7322msvAF5++WXAnUP8LFiwID+DyxGnn3464FSfdCj7OKqttnX8HHLIIZ6Pz89pp50GZKaO6jl6L+W1+lMGooRWaLR6pYYx+VoVDRol/ih3WqjepFevXoA7PqOIrr3UznbNmjVFso11raX5jB8/HkhtGqLrM33PJ3/PBIkpqoZhGIZhGEYkKSjJz1RQUBCY2WnzzTf37kCU0ygVQwpBSUjJkGonpADqrq0kCgsLU0xsmcxP7d6UTaiOK/IOqQWnvyVgedHdWOvWrYHMquiymZ8f/X1btmwJOFWjWrVqnsfKXykttTTdviS/zrJlywBXwZuNAhLE/IQyU3/66ScgodqU1IKyOHbccUfAKcXat+vXrw+UTYELcn7ysCmBorCw0PMeKTNP+3RJaKVC6RbHHHMMkF0rziDnl2uUf6iqVh0PWvlIR5Dz036VvFI0ceJEwCWP+FGqiFpT69wRFPneflLEe/ToUexzGjduDGTXSchPLufXqVMnTwV94403gJKV4tJ4/fXXAfd9pHqIktIqwjr+1MlQqxV77rknEPxqYb7mp+3YoUMHfQ7g1G51xAuaIOen4+a9994DEik1WjUTmoe8q9rXlAefjFJu1MXK/16Z4J9fMqaoGoZhGIZhGJEkZx5VZcENHz4cSFzBS2UqC7r77NKlC+A8Y61atQKKVxeCQn6+Y489FnCVi/LXSGmqWrWqV1VdqVIlwHlY1DFGSHn85Zdf0vYFBnfXku8KXim3UmX0E1wXJ/2Ub1edcvQ30l19p06dvGrpqHTckkKuv7vu6rNRUwHOO+88AJo0aQK4VIqwvIziq6++Alwm3k8//eR5jbKpYNffTds8G0U1F8gvtmzZskA9b1KbR4wYAbhs55IU1SBRZzOdB3bccUfvONM8VWmrLESpsP5UDiN8Hn30Ua/aXytTSoHJpnJfdRCjRo1Kec+w83/TodUIdaJavnx5mMPJml122QVwirCUVK1kzJkzJ5yB5YiHHnoIcCveWqGRwgru/CtvqmpsgiZnF6qaQOfOnUt9rgKsFcmSjAy+/vaW2unzHc/h/zLUEvZvv/3mRTMIfdn5C1B0kunRo4e3FK6LPF34KgpCUnoUUOtU/Xz77beB1Faw4L7M9fsoIfN4EBfOtWvX9i4WFEd15513lvt9g2Dq1KkpP7Ohbt263gVqVNHy8P333++dWMuDbmDU7lFWjuIihHLFL7/8AriIpq5du3pju+2221LGpNg4HZf33XdfXsdqZIaW+v0FUaIs32W33nor4C5Uo8gZZ5wBOLFFN81Ra8qQKbIqqvhb81Kk49133x3OwHJMuuJToaJTNR3JVSSXLf0bhmEYhmEYkSRniur8+fMBVxR00EEHeUviUkOlLM6YMQNwBSnbbLMNkFgiKC7iQUqjlId8oWD3TTfdFHChuOkM1Ipu8PPEE08AiaB8zV0/44QM/PoplbJv376hjak0FPEjlbt9+/ZAYp/L9E5fyv/AgQM9K4iiO/IdJ5YL6tSpAyRiqxS5pmOzPAptLhkzZoxXrDFt2jTArcIUFxW2xRZbeEUF69atA1xLXakIKpq89957czLu0tBS7pAhQ7zHZH+StUjnIimtKmA0ool/yV/KqlR8RVpBNJfyS0Orgmp5qyXyxx9/PLQxlZdq1apxySWXpP3djTfeCATfNjSf7Lzzzt51V6bfg8ceeyznnnsuAA8++CBQtPV6UJiiahiGYRiGYUSSnCmquio/+uijgUSMk+60FPgrZNbVFb1Yt25d6EUpQsqLirvK490LIkolCkh500+pwlFpH1oSn3/+OeD2udatW5faqEEhybfffjsA5557rmcs12NxRJFkDRo0AFz7uw4dOngh1lJUVVwYNbbddlu6du0K4P1U0Ya8m/4Ytc0228wr+pQaolByoZWSfK/clISKUlWcI55++ukwhmNkiZRVFTnKb6p2qUuXLvUUfZ2btC9LfY0iimzUuVXHW1SbhWTCUUcdVaRJjPDXz8QBtYTX91aPHj08ZV8NYObOnZvyGqmnWkW87rrrvPOlVslz1cTBFFXDMAzDMAwjkuSthWo23j1dpUcB+WsVRVEeFCWSr6ibfPHcc8+FPYSMURC+gvG7d+/OzJkzAefx07bef//9Aejfvz8A7dq1A+Ctt97i8ssvz9+gc4Sq5eXXFStXrvTm6r+7jgpanVBqRjKqRNXPkhpT+NH+kau4lSCR11HNT4x4oQp++b/1/bDrrrt66mpx1f16PN/pN+lQvYY//WbChAmAqw8wwkeefPnaa9Wq5aUaNG/eHCgasaiam+Tz54UXXgikRlblAlNUDcMwDMMwjEiSN0W1LOiK/ZZbbgl5JA5lgir3VB5V+WvTqRmqfpdSpTaifo9uXJHKHGekRFxyySVeW0JlwclTfcQRR6S85sMPPwQSamzUMwEVTl2nTh2v5Z+SK4SCnDUv+ZbefPNNPvroo3wNNStUDb9y5Uqv6v+4447L+v2U26z3zbYRRC654IILUv7vV7CMeKIK/+RKf6mtcUD5ov7VDWVtxpmCggJvRaYioaSeU089lW7dugHu3KcGIuKVV14B3Mrp6NGjPWX277//zuk4TVE1DMMwDMMwIkkkFdVvvvkGcJ2dooS/Yv+tt94q9rlSsCoqUa48zRTd7S9btszrMuWv7pSqKA+ZfD3qShYl6tatCzjPkCpwq1Wr5j1HirEUZFXJK380ShXupaFWoyNGjPDUJ616KBNVDBgwIOX/v/zyC0OHDgVcTqpSDaKYiSjPtFo5CiUzbGicc845fPbZZ2EPw/h/rrzyypT/q82yVLc4M3DgwCLe9gULFoQ0muD5+++/vdxb/YwSpqgahmEYhmEYkSQSiur3338PwJw5cwCXVWpEmzZt2oQ9hMAYOnSop67FGfV9l19MlbiFhYWeIixlUf6kioJ8UlKK9VMMGjQo72MKEnnkypPhXJH44osv+PPPP8MehgHss88+3gqGViUuuuiiMIcUCMpR1spUMg888EC+h7PBEokLVcUBqRjCMIzsUKGeWp8aFQfZERSj5rc2VDQUB6diMbUpVrvGOESHbSisXbvWKzyUHaO41sVxQufTb7/91rNV6Ti0VsX5w5b+DcMwDMMwjEhSUFL4dUFBQenJ2DGisLAwJV/C5hcvbH7xxuYXb8Ken1oYy+IRdMFb2PPLNTa/7Nlzzz0ZO3YsAFOmTAFcAWa+2NC2XzKmqBqGYRiGYRiRxBTVCoTNL97Y/OKNzS/e2Pzijc0v3piiahiGYRiGYcSOEhVVwzAMwzAMwwgLU1QNwzAMwzCMSGIXqoZhGIZhGEYkKTHwv6KbdW1+8cLmF29sfvHG5hdvbH7xZkObXzKmqBqGYRiGYRiRxC5UDcMwDMMwjEhiF6qGYRiGYRhGJCnRo2oY2XDAAQcAMG/ePBYuXAhAgwYNwhySYRiGEQJDhw4FYNCgQQDcdNNNAFx11VWhjcmIF6aoGoZhGIZhGJHEFFUjMGrUqAHApEmTAPj333/ZdtttAWjYsCEAn3zySTiDMwzDMPLG2WefDcAuu+wCwLfffpvy04g/m222GQCffvopALvtthsA/kZStWvXBmDp0qVZfY5dqBqBcc899wBQv35977FNNknsYpUrVw5lTIZhGFHmzjvvBGCfffYBoF27dgCsWLEitDGVh+uvvx6Afv36AbDRRomF2+OPPx6A5557LpyBGYGg7/LLLruM/fbbD4A6deoARS9QRXk7oNrSv2EYhmEYhhFJTFE1yk2fPn0AOOqoo1IeX7FihacOvPvuu3kfV1nx3/Vde+21AFxzzTUhjMYwjA0BnXeaN28OwM477wzET1E9+eSTAejbty/glNS5c+cC8NJLL4UzMCMQKlWqBMDDDz8MwEknnVTkOfPnzwfg448/Tnn8jz/+KNdnm6JqGIZhGIZhRBJTVPPIrrvuCsD5558PwIABA7y7lB49egBwxx13hDO4LLjiiisAuO666wDnR12+fDkAHTp04LXXXgtncAEwZMgQwBTVKLL77rsDsNVWWwGwZMkSfvvtt3K/r7xWY8aMAaBu3bopjzdp0oTPPvus3J+TSzp27Ag45QNg+vTpALRv3z6UMRlF2XjjjQFXkCI233zzMIZTbg455BDAzUu8+eabAKxfvz7vY4oqRx99NABbbrllkd/pWI0KtWrVAuDBBx8E4LDDDgNgzZo1nh9Z6v+8efMAWLBgQaBjMEXVMAzDMAzDiCQFJVVjFRQUlK9UC+dTqVevHnvuuSfgvA3NmjUD8CrHNJaCggIAHnroIe677z4AXnjhhfIOhcLCwoLk/wcxv5KoXr06AL179wbgnHPOAWDHHXcs8lypkOl+lyn5mp+U1GHDhgHOuyLuvfdeAC688MJAPzfX8zvyyCMBePnll1Me1/6Ya/K9f+abIOYnpfPMM88EYOuttwZg4sSJnHfeeVmPrWvXrgAMHjwYcKrQyJEjAXf++eijj4p9j1xuv8aNG3sqRePGjYGiqoXOrwcffDAAEyZMKPI+Dz30EOCOzT///DPjMeR6/yxu5aJFixYp/3/llVdKfH625Pv4U5X/+++/r88D4NVXXwWKzru85HJ+devW9byoO+20E+C2k6r9165dG9THpSXf2+8///kP4FYwdK3y+eefe8+pWrUq4JIcDj/8cAAOPPBAwJ1nfvrpJwBWrlzJ3nvvnfbz8j0/KftamTnxxBMBeOedd4BE/NiiRYsC+zz//JIxRdUwDMMwDMOIJDlXVAcMGAC4NmplRf4W+SLKQ77uSHTnoTlLMS6JOCiqNWvWBNwdv/4vJk+eDDi/7Zo1a4L8+Lxtv7Cq/8NSVBXIfdpppwEwfvx4AFatWhXo52QzP/m677rrLgDatGkDuH1QSsWaNWtYt25dmcck76kqkjfddFMAWrduDeC1AM6EILefxqVK6mbNmrHNNtsA0KhRIwBuv/12ANq2bQu4anHtvwrZ9o0JgCOOOAKgTB7yXOyfOqbkBy9mT9/3AAAgAElEQVQLLVu2ZPbs2eUdgkdUFFVtGymUQZHL+c2bN4+mTZumPJbv3NRcbz+1Br/11lsBp4rqnKHtV9I1lbapfO6zZs0C3Pnsu+++K/a1+do/5Zl+5JFHAHd++d///gckak9ygSmqhmEYhmEYRuzIuaK6ZMkSIFERLq+DPIzFeVZUFd+/f3+v5ZYUhvKQ6zsSqTtSFqtUqZL2efLu7Lzzzp6vLOqKas2aNb07qiZNmqT8TneJ3bt3B0r28ZWHsBTVpM/Lxcclf24oiuqjjz4KFL1TXrNmjddtTL6rGTNmpDxHKqY8ViWRzfx0TD3++OPemMBVGJd3X/vwww8BPF+YfNfZKHxBbj/53eQ33XPPPUvt7pKJoqPn3H333QBccsklGY8piPkV5wNPRisYpdGiRQvvXCqksGajtOb7+JMHVWq+ts2+++4LBH8ezcX8dHw+/PDDnrL4448/Ak4Z/uKLL8r7MRmR6+13yimnAPDEE0+kPP7BBx8A8OyzzwKkpIL8/vvvgDt/lYd87Z+9evUC4JZbbgFcNuqxxx4LuGuVoDFF1TAMwzAMw4gdOc9R3W233TJ+rjIRO3fu7D02duzYwMeUC66++mqvYlhV8MqNk//khhtuAFyXpmnTpnmKatQ5//zziyipr7/+OuB8g6tXr877uHKBFJ1sVLUoU61aNQBuvvlmAAYNGgS4SnB5HEWdOnW4/PLLUx6TL1Lqj/LzpJANGzYsUCXomGOOSfm/xhzEZ+y7777Uq1cPcKs76Srlw0CrMXvssUdO3l/Zx/nGf0xJ+WzZsmWxr5EKq9fq//5/Jz8nX0kd5UHqfZxRco/UVIBzzz0XyJ+Smg8uuugi73tBKxby3mpV4uuvvw5ncAHjz1oeOHAgkDslNRMiFfhfuXJlwIVsRxlFN9x0000AnHfeed4Fqnbkyy67DHDLbGLixIlA8PEjuUCFNslRU9phdeBWlAvU0lDhRxwbAOyzzz489thjgLv40XK3lpdl7RC1a9dm//33B1yhlW5KdCG13XbbAc42cOihhxYpsgsS/7FUHo4//nivcEAX4FH5stHN7amnngokYv7+/ffflOeo2E3Fbyr2UAROOv773/8CJRdt5JOSoqZ0fvRfjJZEkMVVRukoJg6chSHoIrAwadWqFZAQzPyWmrfffhtwcXE678V9/iraFBdffDHgrA1hYEv/hmEYhmEYRiSJlKLqj7cAt0weNRRqrCgmgC+//BJwy09aUhVaZuzUqZP3mJZH7r///twNthxISU0u8rrgggsAePHFFwF3ByaF7qyzzgIS85X6qr/Tzz//nIdRG0LH1IoVKzz1Q/FpandXHF9//bWnMEptlT3A3yYxX8h+Up7WvDoOk1W8MNWCdMjy5Fe5k/nnn38AV7yiIojmzZsX+xpFIYWFVmH8y/klIZXUr76+/PLLRdTWTAuxwkTnyy222CLkkWSPzgPJzV5kt5DCWBpr1qwJvNVm0OjYWrhwode8SCtSsiGJv//+G3CK6vDhwz2VWcdqHNCqi5qdnHDCCYBTzI866qi8j8kUVcMwDMMwDCOSREpRlWlXzJ07N7KeI3+7xi+//NILCff73HT3qaBg+eLWrVvnqQN+9TUqKCosmZUrVwKuhVzPnj0B55FLxw477AC4dnNhGrM3JLR/devWjWXLlgHujvnXX38t8/v98MMPgY0tm89TEZDmpYI+FS6CU6yKa7ShqDsdh1FECkxQXtKvvvoKcNF5YaHzudQ3KaJHHnmk9zs9VpoXPJ13NarfF8koCD9T5TGKKKpJBZrgCuLkry6NVatW8cYbbwB4/vnp06cD0fl+UNFmw4YNvYIxBf2rPaqKj+QNT96nVUDtV1+jjOJDFy9eDLhtovkdd9xxAMycOTNvYzJF1TAMwzAMw4gkkVBUFSSrOxUxfPhwz/cRNVQFLZYsWeJ5juTb0Z2V4oDU+lDcdtttkVVSS2LcuHEA1K9fP+PXKPxZ85X6bOQWVaqq8Qa48P4rr7wSgKeffjr/A8sQ3c1LkfAHxmsuyR4wJRLUqlWr1Pf/9ttvgcxVoCgjb3gyig+TChI10gX0l6aKplNSS4q3ihryOvojtD799FPArVhVNNSsQ+kVW221lRc/p59aebvxxhtDGGHJaNVGqzhC/nbVcfTr1w9IHI9qIf/ee+8BJXvOo4bOsWrjrEYHUlzr168feJvt4jBF1TAMwzAMw4gkkVBUlT+mSmKpI2qfGkW23XbblP+3atXKa8eYKdOmTQtySIEilVs5mcmURUn1c+ihhwLuDvr555/P+r1yhbxx2eQ4Ro1LL70UcM0mALp27QrAOeecA+BV3kpdjBKqUm/QoAHgPJZKmCgpEP/jjz8GnOKxaNEiwK0IbLTRRtx5551APLOA5cWVt1+tN5PzHtW2uiKFrycnBajKPw7eVCFF0Z/LqTl8//33+R5SmZG/XSuem2yyiec3VS2GH6n7UiYbN27sJVWoMVC3bt2AaCqqpaEagN69ewOJ1qPKTFflfJwUVSFlVV7cWbNmAXD55ZfnrXGIKaqGYRiGYRhGJAlVUd1yyy0BirRplC8tyFaMQXP99dcDMGrUqDK/ViriO++8E+iYgmT77bcHUlvj+fnjjz8A+OmnnwC47777AJfjeNhhh3l+QaGOXnHoPqbcRn/mY5Q6U0n1VbKEFDShBIrkJIo333wTcN6jvn37Aq6TWhSRIihFXq2Hjz76aCCxvz7zzDMpr5Gi+ueffwKuUlkeQYCpU6fmcNS5RS2npUppXlLsXn/99SLn1jiTXE1thItqDfr37w8kVjh23XVXAD777DMAPvjggxLfY+7cud53oN/3WRHwd3gKE50b9H2+bt26Mr+HMu2lol999dWeb/WTTz4JYpjFYoqqYRiGYRiGEUlCVVTleWjYsCHgrvLVESHKjB49GnBZY6eccgoHHXQQ4JRgeWz79OmT8tqbbroJiHa3igceeABwXqFddtmlyHPkxbnnnntSHpdX7sknnyyiqMrbFCdPWZTR31HKhDxQmdwxa+VCeaNSZfOdlZoNUm30MxOqV69e5LE4dkqTiqzjT15Hv/dx2LBhIYwud8Q1N7Uio5XF+++/30vZUMpN27ZtAfjrr7/SvrZmzZpMmTIlD6MsHa3wKbFHGeHKWi4oKCjiKfajFAel+7Rs2dKbe9ipIlK758yZA7gs7fHjxwMukaEkfv/9dwCeeuopANq1a1fk+z1XhHqh2qpVq5T/q13ZW2+9FcZwyoR2Wm00/UxGO7vQhtY8444KotTWUsg4riK5ZGQXWLhwYY5Hlzu09B8FC4AKoLQNFDWlL4vk+BAthesxBd+rQCmOBUUbIkOHDgWgWbNmaX8/YcIEoOItp/rbrc6ePdsuVENGVqMbb7yR2rVrA+57QQHxuon+7bffABeUf8EFF3jnIN1k5duKowtINV8o7sKrLBeqyc+7+OKLgfAbbXTu3BnA20YqeNPN7jnnnOMV8SkezX8TL9ueClnziS39G4ZhGIZhGJEkFEVVwfi64xKdOnUKYziBozsrFasIqYhxUhOlgBYWFhYJqJZ1IxN0lxYFFTJTpNb4lZwoocI1FURddNFFgAtAT77Ll6Iq5VRqhtoVdujQASBvIc75RsVHYuLEid4qRxw4+OCDgeIjubTdBg8eDFQchVzxOH7iFPJf0Zk+fTo9evQAXOHOCy+8ALhVHxXeqKgxuSHH2LFjAVcYmC90/sxULc3kOa+99hqQiE578cUXyznCYKhcuXLK/6Vg16hRA3CRU+DaNkttVlGctpsKWWfMmJG3axlTVA3DMAzDMIxIEoqiqlZ/uqNSrMz8+fPDGE7gSPHo2LFjyuOnn356GMMpF3vttRcAd9xxhxcUv8kmme02hYWFnu9FwcBqvxYH4uB/U+yUvEb+oOyePXsC0KZNmyLeIikAKgisSKHw6ejSpUvK/ydOnFiqkhIVbrnlFu+8kq4JB7jCiIrigRf+Iqo4HJfZIMV8p512AuK1HXv27OkdS927dwfc94RUO3/8XfL3gxTVfKNIxTZt2qQ8rmMsOcpO89MqjAqlZsyYATjFWPGTxRWRRQE1X1Cx2NZbb+39ToVX8rXqp2Kp1BZ21KhRWcVcZYMpqoZhGIZhGEYkKShJUSgoKMiJ3CAPhO4cFYV09tln5+LjPAoLC1OMJrman9qmnXnmmQCsXbsWcNWOujMJmlzPTzFi7dq1A6BevXqA8/lIoVPEx7Bhw4pEV5WHfG2/NJ+b8n+1bQzab5vr+clb5FcPFEflbxYQNGFtPzFv3jzAVfgefvjhXvODIMjF/NTQYfbs2Z6vzI8aUxx11FHl/bgSyff2k5Lq96jKmxq0spqv+R122GEAPP7444BrriL1TvFGQTe8ydf8tAonf6YUOqEK+Ndeey2y3w/HHXcc4L7Lknn77beB/Cve5Zmf2p5nEsyv2Eyt1qkF9fDhwwF3/RY0/vklY4qqYRiGYRiGEUlC8aj6PY66Yo872267LVDUUzVixAggd0pqvtDdWK7bpRm5IZuQ/IqI5h+kmho0arChpgz//vtvEWVf/j4Fq1c00uWmJv+MK6oK1zxUyxAXv3RpLFq0CEifox0XZs6cGfYQAkVtzhX4f8QRR6T8fsmSJV6GrZrGROn8aIqqYRiGYRiGEUnyqqgef/zxgFMehaqO4448qbqTXLZsGRCvSnejdOQbNIxcsfHGGwOw5ZZbFvmduo/JI11R8lL9+Fem5MWtKKg6XrUadl4xcoVyzNO1IY4DpqgahmEYhmEYkSSviqqyuqQWqApXP+PMJptswoknnpjy2NKlS1N+GvFGnrK43pUa8WXVqlX88ssvgFNSFyxYEOKI8o8URx1/cfeqKsvSzieGUTKhFFMJmccrgom8UqVKXktKxcgo5N6IN5m0zzOij1rIVqlSJeSRlJ3x48dzxRVXhD2MUNEFnbVONYwNC1v6NwzDMAzDMCJJXgP/t9pqKwCmTZsGOGXjkEMOCfJjiiXswPFcY/OLNza/eGPzCxYF/fuV1Fwt+dv2izc2v3hjgf+GYRiGYRhG7AilhWpYbGh3JDa/eGHzizc2v3hj84s3Nr94Y4qqYRiGYRiGETtKVFQNwzAMwzAMIyxMUTUMwzAMwzAiSYk5qhXdA2Hzixc2v3hj84s3Nr94Y/OLNxva/JIxRdUwDMMwDMOIJHahahiGYRiGYUQSu1A1DMMwDMMwIoldqBqGYRiGYRiRxC5UDcMwDMMwjEhiF6pGYPTq1YtevXrxzTff8M0333DLLbeEPSTDMAzDMGKMtVCtQIQ9v3T70iGHHALAm2++GcT72/bLI//++y/XX389AIMGDSr3+0VtfkFj84s3Nr94k+v5nXnmmQBMnjwZgAULFgBwyimnAPD1118H+XFF2NC2XzKmqBqGYRiGYRiRpMTA/2yoUaMGAN9++22ZX3v55ZcDMHbsWAD++uuv4AYWU7bZZhsA+vTpA0Dfvn0544wzAHjsscdCG1c6li5dCkDNmjW9x2rVqgUEo6iWh6222gqAs846C4BjjjkGgLZt2wIJNfitt94C4OCDDwZg6tSpAFx77bUAfPrppwD8888/eRp1uBQWFnL11VcD8N577wHwv//9L8whbbCMGTOGbt26pf3dRhsl9IZ///035fHJkyfzwQcflPi+M2bMAOCzzz4LYJTBUqdOHQB22mmnlMc32STxtXX22WdTpUoVALp06ZLynEsuuQSAu+66K8ejNDYUjjzySMAdZ/vuuy8AjRo1AnKvqG7ImKJqGIZhGIZhRJLAPKrNmzcH4NFHHwWgcePGAPz0008ZD2bVqlWAUw/vvvvujF+bCXHyeBxxxBEAjBgxAoADDzwQSKh8PXv2BOD7779PeU3Y85MS+cYbb3iPSfXt1KlTud8/m/ntsMMOKWOSSpMNjz/+OAC//vorAP3792f58uVZv5+fsLdf7dq1AXj77bcB2H777T3fsVS7pk2bAk5hLQtBzq9Dhw6AU72TefXVVwE4/PDD9bmlvt/HH38MwNChQ1PeN5PXilxsvz333BOAuXPnst1226V9TkFBgT6/2Pcp7jma93777VfqWHIxv+OPPx6Am266qcjvdtllF4Bi552O2267DXCe6tWrV2f82iDnt//++wPw+++/A4mVMSnB1apVAxLnD4Ddd98dcOeX+++/H4D58+ezbt26bIdQhFyfXw466CDAqfRi1KhRgFsp1fkzaHI9P50fFy9enPL4ySefDMDTTz8d5McVIezvB7HrrrsCcP7553v+3CZNmgDw888/A+57tyyYR9UwDMMwDMOIHYF5VKUsVa1aFXCewEwUVd1Vy2+UjVpTUTjssMMAd3emu0+pzGPHjmXt2rXhDK4UwvahpmPrrbcGyqekCql44oADDvD23WXLlpX7/bOhfv36ACxcuLDc76EKfyk+Z555JldddRXgFNXyfE6QaIXB78sEdwyl+11xNGzYEICHH34YgN122w2Ab775plzjLC9t2rQByqYqlgUdH/lGK25SETfffPNyvd9TTz0FwD333AOUTUnNBTp/6zutSpUq3v646aabAvDHH3+kvObiiy8GnL+2TZs2PPvss3kZb3np2LEj9957LwAbb7wxACtWrADceeW4444DoHXr1kD8alDWrFkDwIcffgg4b2pFp3r16gCccMIJAJx++ukAtGrVynuO9m1d/3Xv3h2AO+64I5AxmKJqGIZhGIZhRJLAFFV5EhYtWgSUrQJOd5byT0mN3ZBQRps8Vr/99hvgMtriqjIn+1WjhPy9EydOBOCll14q9rn16tUDoEePHgDss88+QMLX1759e8D5r+LEFltsATjFo127dgDMmTMHgAcffNDzQ+u5UUFJDIceeijgVNSKhhSKZKRUSbUo6bUDBw4Eik8G+OGHH4IYZsZIYZRSn05J1blv/fr1gPtOUQKKXvPuu+9yww03APDFF18ARVXKsJC6KI/qp59+yieffAI43/HMmTNTXqNUkb322gtIeP6jrqj26tULgJEjRzJlyhQg4V0Et69pNXDYsGEAXnrFf//737yOtbxo1VjqvRTVK6+8Esi9RzXfqO5o9OjRgPOxSwmfOXOm990xePBgwKnlP/74Y6BjMUXVMAzDMAzDiCSB56g2aNAAcBWbmXi8XnnlFQDeeecdAJo1awbAyy+/HPTw8op8kV999VWxz5ESJCVVqsGAAQMA54eJA6r6T+bWW28NYSQOZbuqmls+sNmzZwOZdVzSfihVW6+VOhQm5fGMStVS1eoTTzwBQO/evYGEd1X+1bC9mn7+/PNPAE8xPPTQQ4sojKrc92eESrG69NJLi7yvFLmoZOVq/0yu1pePNl3igR8pe2Ejr+3NN98MuO8J8fDDD/PII48AiYQDcMpxHCnpvOKvGi+O0jJww0SrSlITn3zySe/483tPleigfUCrNOPHjw/dSxwEe+yxR9hDCBStrN14442Am59WHZXsJD84lK0eIBtMUTUMwzAMwzAiSWCKqirRVfEnP0NZlBh5eC688EIgUTGmbNU4ISVA8znppJOAovlx9erV8zxIm222GQCnnXYaEL2uU5kgJS5KKIdQnkZ5SbNRzKQiJCupqtCNk0dVd8xS7ZV9J3VSx2zTpk29ueqxqHgAhfy0c+bMYfjw4SU+V97GkrxxEyZMAOC7774LaIRlQ/78J598EkjvLVVObByoVKkSgLdt/Eqq1O5zzz030MzQOKFEhy233BJwXkjt21FEfn1VhF999dWeH7c4VK8g72P9+vW9VdQ4o/ocnU/l24wbWlmT11i5vy1atABcqs/ff//tvUZdKI866qicji2wC1WFhCumRyZ37ZxlCUbXkvlWW21V5EJVX5xR+8IEvHaTKoDQcr5/eUNh/lOnTvVOTtq543iBKjp27Jjy/ygWUmUSl6aWjddccw3gArl1gk1GsUa6wdDyZZRR0LiWk3Ws+m0E/fv3955TEVqn6hi74IILivxO7XMzsYLkAjVSmDZtGgA777wz4C5Qy9J4IEooimnvvfdO+3tZONq2bestLepLT9FVuV5WDBtZ3RSkriVX3UBGCe2XWsZ//fXXgejE1oWBmmnopiyODBw4kH79+gEuhksNl2TF8bPLLrt4Nyw6znOFLf0bhmEYhmEYkSTwYipFbiiiQrEFl112Wamv1R31OeecA8DRRx/ttZOTpUBxQA888EBwgy4HGtduu+3mKaqKPtJ8JJUrXFtL5NWrV+fBBx8E4rlcoLaoMsf7iZM6vMsuu3DFFVcALmJDamlJ6G5ayz5SxKK4jKnAbbV3fP755wGn/Ptp165dbJW8ZFSw2KVLlyK/03KlVIPSli9zhRQJKVYloeKGdMWL4NqiRsE2JTVUf1d/gwFZxB555BFPyVEUmsYvNV+rcoqtmjRpEl9++WXKY3FCS6vjx49PeXzBggVhDCcjjjzySMCpv5msQGgbn3322SmPH3vssRVi6T/OaLm/X79+nh1Oj7322mslvvbyyy/3vjOFvi+CPh5NUTUMwzAMwzAiSeCKqq6wddd/7rnnAgm/w0cffVTia6XGqujo1FNP9YqNhgwZAkRDJUjm8MMPBxLq6eeffw44v+LKlSsBVxAhz0fbtm2BhIoqJTWqbVHTISW1ND9msmc17Jiq0li0aFG54qbkwZPxXGpllJg0aRJAxr7TwsJC77mKrooT8hZL2VfRQzIqhlPkU1icddZZGT9XwelCqr62leLUVq5c6RVlzZgxAyha0Jlr5EFVFFrXrl0Bp+on428qocKy4v42/fr18wqOzjjjDAC+/fbbAEadH7bddlugqB85at9xyej7WNtVf/fHHnvMe0zxRSq0UhGqvw1wp06dvOiq5AIdI/dsv/32gGt4U6VKFW9ltDQl9YADDgCKKuTgvud13gkKU1QNwzAMwzCMSBK4oqr2d1INFVr81ltveaqo/HtjxoxJea08SA899BAAnTt39rxjqi5MF9IdBvLoJCtNLVu2BJxHVaiFnJRI3bHcdNNNsVJSRaaV7YcccgiHHHIIADVq1ADc3yLO3HnnnUCihZ5WDpQUoP0iarRr1867i1Y1sRR+P4qHKygo8KKQypLakU+UEKK/PzjlVKs7O+64Y7GvVzvH008/PaPP69ixY6DHbOPGjQEXYRcEOg8BdOjQAYD58+cD7pw7efLkwD4vE+666y7A7XNaZdLfvXLlyl6zk1NPPRVwzQrURrp27dqAa0EKLkFFTTgUOv/cc8/lZiIBonlKEZeS6m+tGiWkyKu2Qgr5Cy+84O1bqiMRfsVf/2/UqJHnWdbqY5xRgo/qVqLSNCQdOk7097/zzju9Y7Q45KNX0w59nwCeV/y6664LfKxgiqphGIZhGIYRUQJXVIWqPBs1agQkQrblU6pWrRrgqo1HjRoFuMrb3XbbDUh4WhYtWgRAr169cjXUrFAWo1SA7t27F1FSlcOp5ANx5plnAkXzVaPOLbfckvVrFQwcR6SAjxgxAnBqzV9//eXtlyNHjgTc30gZsmHnC6oF6uTJkz1FQ/tfaa8pLCz0MlajhnxvzzzzDJC9kq1EgEw5++yzGTduXFaflQ6lo6jltJ90gf/ZPEe+svvuuw9wQfsK8c4XGqN+yjedjL9xg5ppSLGSSty+fXtPia5bty7gmjm0atUKCK9xQyZo2+u4lFIVB9Q6Vef1I444wlNZ/UiF1bnkxBNPBFx9R0VBHk9d10St7TS4jHet7iqP/qWXXvLOQclKKbgVGm1zXb+ByzpWkpFW1IPGFFXDMAzDMAwjkuRMURXy3Zx//vlex4169eoBzqMjdUSV03pNYWGhV62aqyv1sqK7elU7ym/bqFEjRo8eDbg7DnmrxJIlS4BERypIeHWUL1tapV3Y1KxZs0iL1KVLl3q/Syb5efKoRt2betlll3l3+Kps/fDDD4GS24fefvvtgOumc8IJJwBu3mErqvJwVqlShffeew/A++lHHkD5BpcuXVrsc8NGalTQnmB54VWVrrarIuj2pfqbF5dXK+Xxm2++8dRPKVTyg8mnqboAUaVKFa+DkL/DlbIt862olgetQEmFnTRpEl9//TXgzkF77bUX4FJnoqioasXC3042Tt3f9H18/PHHA4kaBO1rOmbUBXDWrFkp/z/wwAPzOlbDoe2m72jV2EydOtWrQ1BaQ2ksXrzYqwPIddqGKaqGYRiGYRhGJMm5oprMF198kfKzuOpGVZel660eNsp7U8cs+f0uvvjiIs9dsWIF4DqPvPvuu4BTbZYvX573XMNsSe4+JSVVmWnyGIvkzNSo56eK++67z/PvlQV14PBnHyb7eMJE3dIKCws9P21xFfyq4NXYO3bsGNlqf/lEtWpRq1atUl+jyuJ0KqK6OclXLtVrs802S3mePPNBoeNDmZo6r7z//vuAq5Bevny5d970o5QU/RQ77LCDd87xe2C1IhSk3zYT5KdV2kGuFHtlOCsNIEooL7Zy5cohjyQ4vv3224z3pUyO1Sij1BtVzsv3GSd07aUV3ZNPPtlbfZPyrWNV50Lx119/AYkc+Hz5cPN6oZop+vJ/9913U8Lxo4Ciaa699lrAjatFixa8+OKLgBu/lkUqQpu4dMVQ/gtUFRAZ7mAOCy0Hy87w888/e8WKfmSeVxtYLQdFeSlSlgpd6JV0Q6QLVFkaMmnGkK82lgrnL0thiQo4S7vJ/emnn7wvVX8xqr6UZAHQhXGu0QWkttcxxxwDUGozmHTst99+xS5TRrlQVUKMkD2hIkQ0ZYIsfr/++mssW99qX41im+yyIhHN38YX3LHqb4ai7301C8gHtvRvGIZhGIZhRJJIKqpi0aJFtG7dGnDxKlFTJxXuO3LkSK9QoUePHkD0xloektuhFhc1pWiYOFK1alVv+ylaLROkTDVt2jTlcdk7wiI5YgooMWaqXbt2gFNSy9LOM2z8bRmT+eWXXwDo0qULkAglj4BUJJAAABiQSURBVDPa1+655x4A79xYHNtss43X0tfPV199BeRPSRVSDWV1UOHi1VdfzbRp04DS1VAV64wbN85re+wvRtP7RhH/8aUiWy25biisX7++2CLCOKCW6elaM8cZrbL4v89VOBxGO21TVA3DMAzDMIxIEmlFddq0aV7L1JNPPhmIjkopQ7yC3vfbbz8mTJgApA+xrsg89thjADz66KMhjyR7XnjhBc88LjV00KBBQPHRaJtvvrkX/q/ItaighhQqxkmOVZInVQU1asSh+aaL4YoK8vedd955gCsGSEbezf/85z9A/JVUMWXKFACaNWsGOGXD37JShVMzZsxg3333BYo2BVB4d77RtpDKrTlNnjzZU2zSbdNk7rjjDgAOOuigIoqcCuUULRcH1N52Q2Px4sWR9hKXhr77/U1DdG5SYWZcUPSmmqioLbXOp2qRW1xRZy4xRdUwDMMwDMOIJJFWVOfOnevFwUQtykNtxdTOb/ny5V4Ad0WoBswExVSpHVucadCgged3a9KkCQCHHnoo4Fri+UON+/XrV0RJfeqppwDnXwobKU79+/f3WhyqalMxMXpOGN6jstK3b18Ahg4dWuxzpBBXFCVVaDvppxJRkv3j4JprNGrUyHtucuMAKD6iLF8oUULj2WOPPejWrRsAP/zwA+BWaLQqoFSK5BbA+p0qsRU6L39yFFHqhCINMw1YN+KBVjziRL169YooqYriVBONMM8ZpqgahmEYhmEYkSTSiirA9OnTAbjwwgsBuPnmm4HwKiRVTS2/xj///ANAt27dvEraishpp53mZTJKSfV7c+LMk08+SefOnVMek7Jalry4O++8Ewi/5a8qw++66y4gUdkvL6NUNoWtK+83bJUtE7baaqtif7d48WLAheZvKCjnsKQK6u+//x5w6msYPrN0aEXq+eef95QcZVNnkp2tOWvVI8pKqtA8hTJ0pQarbbhh5Bo1NBkyZAi77747AJ988gkAgwcPBqLxvWCKqmEYhmEYhhFJIq+oqgWeWkEqg04Vd/li1113BVw+n6poDz74YAC+/vrrvI4n3zz66KOxruovjTPOOIO3334bKNpxKxOkahXX/SnfyG+qzj9qNZr8u0suuQSIxh1zEGi1Rd3jNnS+/PJLz9cpz3S+c1NLQ97So446ilmzZgGw8847Z/Ta1atXe0kV2bQ/DgslMYgaNWoA0U7byAXbbbcdlSpVAsLv5JcNOpY++OADwG3XyZMnhzamsqKOdUrhAOehjlJnQlNUDcMwDMMwjEgSeUVVd9lSMnfcccdQxqE+4boDlGe2oiupGwqFhYWe71geMVVByrvjZ/369Z6/87XXXgOiowxIJT311FNDHkl+WLp0aV57T4fBk08+CRRfVbxgwQIAHnjgAQCefvrpyHhRS2PhwoUccsghgMsA7t+/P+Aq++fMmQPAZ599BiQUnyh3oCoO7aeq+l+zZg3gOlRVdLRy1bJlSy+7Ux3L4oS++1XLEEd69+7t/VuK/tixY8MaTrEUlGTALygoiG9/szQUFhYWJP/f5hcvbH7xJoj5Va1aFXBRYfPmzQMSsVVhNwOx7Rdv8jW/2rVrA+6GYuHChYC7QM8VUdl+CsK/7LLLvL/FqlWryv2+UZlfrghyfhJf1Gxi5cqVXjGxmmbkG//8krGlf8MwDMMwDCOSmKJagbD5xRubX7yx+cUbm1+8sfnFG1NUDcMwDMMwjNhhF6qGYRiGYRhGJLELVcMwDMMwDCOSlOhRNQzDMAzDMIywMEXVMAzDMAzDiCR2oWoYhmEYhmFEkhI7U1X0+AObX7yw+cUbm1+82dDmZxhGNDBF1TAMwzAMw4gkdqFqGIZhGIZhRBK7UDUMwzAMwzAiiV2oGoZhGIZhGJHELlQNwzAMwzCMSFJi1b9hGBs2VapUAaBt27YADBo0CICGDRsCcOCBBwLwzjvvhDA6wzAMo6KTtwvVLbfcklNPPRWA8847L+V3hx9+OAD//vtvsa9//fXXAejYsSMAP/74Yy6GaWxgbLRRYlFhjz32oH379gDstttuANSpUweA/fbbD4AlS5YAMG/ePADGjRvHRx99lM/h5oWtt94agDZt2nDFFVcA0Lhx45Tn6Hj8+uuv8zs4g+bNmwPw6quvpjx+3333ATBr1iwAHn30UdavX5/fwZWDPffcE4Bu3boBcOmll6b8Xsfq//73PwBOPvlkfv31VwBat24NwHvvvZeXsRqGkT9s6d8wDMMwDMOIJAWFhcVnNgcZ6Dx27Fi6du2a9ne6U/7rr78A+O677wDYbLPN2GmnnVKeM3PmTCCh9pSVOAVWn3HGGQBMmjSpyO823njjtK/J9/y0LHzVVVcBThFp2rQpAD169OC5554L7POCnN/OO+8MwF133QXASSedVOb3+O677zwlZ+HChdkOxSPs/fOAAw4A4M477wQS21GrHO+++y4ATz31FADjx48HYNmyZRm/f9jzkzJ+0UUXAdCiRQsaNGgAwJo1awDYa6+9APj+++/L/P65np+O+xkzZgBwzDHHlPj8hQsXcsMNNwAwZcoUoORVq9LI9fw++OADwNlK/BQUFGgcRX43efJkoOhqXVmwwH/DiCamqBqGYRiGYRiRJG8eVSkVALfddhsAv//+e8pz5DcaPXo0kPAITp06FYAmTZoA0KxZMyChhgC88sorORx1/nn55ZcBaNSoEVBUARk+fHjexwTQoUMHTj75ZADq1asHJHzHAPvss0/a11x77bWBKqpBoP1wzpw5AGy//fbFPlfKzd9//w04RUvq/q677soTTzwBuL9BeRSrsFCh1LBhw4DU7Tl48GAAbrzxxvwPrJxsscUWAPz3v/8F4Pzzzwfcdi0oKPD+vXz5cgB+/vnnfA8zY6QIl6akivr163tKo3zVixYtys3gskQ+8M6dO3vnleKQyr1q1SrAreAAnHXWWYBT+IcMGQK4VTrDMOKLKaqGYRiGYRhGJMm5oiqPadWqVfnqq68AGDFiBFC6v+2rr77i8ssvB5wCVrVqVSB6imrjxo09xXH27NlA2cZ2yimnAHDEEUcARZU5/e2kMOeLa665BkjEEskj5ueFF14AoHLlyoCrSt5xxx1zP8AM0dg7d+4MOCVVitro0aOL+Ex/+uknwFUZqwpZyuPWW29N/fr1Abxtr+fGAR1LUk39yvjAgQNDU/CD4O233wbwfKjyvt99990AHH300d7xpur4KFfJH3/88Vm/Vsq/1Fj9LcKmR48eAPTq1auI9/Sff/4BnI98woQJANSsWROAK664wlNX9bfp27cvALfffjsQnXkahpE9pqgahmEYhmEYkSTniupxxx0HwL777uspUWWpFJavLKpIUXv++efZdtttAafSZaKonnPOOYDz0RWH0g7ef//9bIeaFar2liIDeNmhc+fOBZyv78knn0x57eLFi/MxxIzo1KkT4LxrQvuklOOSGDNmDOAq/B999FFPldx9992DGmrOUVrDY489Bjj/t1T7m2++GUhU9peUChJVLrnkEsApqZrDscceC8Cnn34KJBRV/S7K3lSASpUqeXnTYt26dYDbXt988w0Al112GeB87uD+Fv379wege/fuuR1wKWyzzTaAWxlLh5RUraoJnQOffvppT11VxrGQ73rs2LHBDNgwjNAwRdUwDMMwDMOIJDlXVL/44gsgccebjRqoXLxzzz030HGVFymp06dPB6BatWplrvg+5ZRTPCVVFcpCVcj9+vUDUhXNfKLcTP1MRgryLbfcAkCtWrUA+PPPPwG47rrr8jHEElEl8ciRI1Me//jjjwEYNWpUmd9TntwhQ4Z4208eOf/nRAn5MYcOHQq4jnBKNRg3blzKz7iy//77p/xfytwnn3wCwMUXXwwk/h5SVLPZD/LJlVdeWaTa/9lnnwWKrhJo/3zqqadSVNUoIZ+wttVGG23knT/V9cyvpKZj6dKlgEuS6d27NwAXXHABANOmTQPghx9+CGrohmHkGVNUDcMwDMMwjEiSc0VVPsaTTjopqwy/6tWrA64KPuycSvU8f/7554GEkgoJRUAePylWxVFS1ylldOr9lYMYBbbbbjvAqRXqyS2fmJDKrEzYMJE3b9dddwVcFbDUKX+Wb1mYOXNmbHIaq1SpUqySeuKJJwJOiYs7Uo6FVifU8WjAgAHe7x5//PGUn1FDHdQuvPDCIr9TBzE/8qqedNJJ3kqIlFV1UlMG8urVq4MdcCmoa51WIKRo//vvv96/FyxYUOb3TX4fSNREgFvlMUXVMOJL3gL/sw2aVqGAUFOAfMdSKbpHX2ha9taJcfXq1aUW5KiwTMvF6S66H3jgASCzZa98svnmm3sXoIcddliJz1U70p49e5ZaJJZrFBL+yCOPAPDMM88AwXxxffbZZ16ETtR57LHHilyg6sJVF6gKX1exFbh9NIgWsflCNhm19m3fvj3gLlSTb1rCLioqDVmfkm8Gde5Ty9Hi+OabbzxbzsSJEwFnhalUqVLQQ82ITTfdFHBRdsmsWLECcFYNwzAMsKV/wzAMwzAMI6LkTVHNhoMPPthTQ8Rvv/0G5E9R1VK/lFQtJfk566yzPMXRj8L81eZPkUbpOPvss7Meay7ZZJNNvJaFWmZ79dVXARf306ZNGwBq1KgBJKKfFPovtVmROvlC8Vr/+c9/An/vc88911OGxo8fH/j7B4Hif4488kjvManABx10EEARpVyrBcnPlSIthS6b5dl8oSI+qZFSUvfee2/A7b89e/aMbCyVjhsVfgGsWbMGcNYFNaSIEzoHpkNRfSp0DPLz3nrrrcDe0zCM/GKKqmEYhmEYhhFJIq2obr755kVim/KNipqSVSZwXlmFa6dTUxVqreIjeVSTvan+94kqq1ev9qJkttpqK6CobzG5+QEklFXFa+2www5A9Bs4ZILmf9VVV3ktN6NWrKG/txoabL755t7vNttsM8AVUZXExhtvDECXLl0A1yq2VatWgGtTGiWkPEpN13En5GGNagEVwF577QU4Py0kPNHg4psyobgVoLDYb7/9iv1daZ7bbFBRlWEY8cUUVcMwDMMwDCOSRFpRBRfXpJ+vvfZaXj9f8VP+Cv21a9cCLgomHaNHjwYoEtSd7n0UG6PnykcnpbY4/2s+UbRTcUhhlYI8Y8YMb7vJI6pIHaldceS0004DYI899uCll14CYPbs2SGOqChSv0tKaFB03IgRIwDXnEN0796d5s2bA06Z0gpHnz59APe3iCI6hvSzoKAAgDlz5oQ2ptKQ2q3EArFmzZoyN5M4+uijvZapYaMWxgcffHDa38+fP79cUVnatjrfhB1jaBhGcJiiahiGYRiGYUSSnCuqymZM5ttvvwVcnmNxVK9e3bsznjlzJpD/fFHdofvZZZddAKfOJLcALMt76X1U7e9XBPS41MoRI0Z4mYhR5bnnngNg3rx5NGvWDHAeSe0PcVRU5ffs27dvkceiRrp8UO1TN954Y8pPtbz1c+mll3pNHl588UXAeQyVhrHTTjsB8OOPPwY19HKjfezAAw8EnNom1BBgzJgxeR1XJlxxxRVAUV/tCy+8wEMPPZTRe2ibDBs2LMWbDM6XW55GF9ngV7f9zJ4920t0Kc/7ax8v7nMMw4gfpqgahmEYhmEYkSQwRVU5jcptFIMHDwZSPUPybuqu/v333wdg2rRpKa+96aabvH8vW7YMcFXy+aIsrVsz9UVl817KML3nnntyqqhKKdN28+fYloXZs2d7iqryOFetWlXOEeYf+TClpEqB7N27d94906Vx6KGHAq5FpRg5cqS3KlGW1ra//PILkFDHwSmqSgMobsUhTNQNTe2Xpa6plar26e233z5yOarJVf7JPPzwwxm/x4QJE4D0flBlr5a2mlVRiKJqbhhG2Yjet4xhGIZhGIZhEICiKg+UOoCceeaZpb7GnxkqhcqvbiSrC1deeWW5xpktUnnbtm0byufnm969ewNwwgknAK5qPBvlUB25wClzyliNMqq81t9CldPqUy5F+4477sj/4EqhV69egPNl/vHHHwDcdtttfP/992V+P23Drl27pjyuTlzZvGeuUKW8/1i98MILAefJ1d+mRYsWTJ06NY8jLDtff/014Hzf6ZAnVdsouQuZWLx4MQC33nprwCOMJtrvlT1rGEZ8KfeFqk6S/gvUe++9F4A33ngDKNncft999wF47SjTsWLFinKNM1tUzKQAbtkR9P9FixYB0LJly1LfSxFGyX8L/Z0UhK8v0UqVKgHFx7nkCn0x6vM7dOgAJFoQZrpcKPvAbrvt5j325ptvBjnMnFClShUAHnvsMaDo8rkKp6JaQJUOLXdn07p2hx12KFLUowuAqMVxbb/99lx00UWAO75kdZg8eTKQsM0k//7www+PzIWqGor4W/2+8847AGkLjXRhOmTIEKB42wDAqaeeChSNIIs7aqrit5xpX4hyq1/DMDLDlv4NwzAMwzCMSFJuRVUhzR9++CHgii2kpE6aNKnU91BBhtRFoUDyMNH8FKek1pFaEm/QoAFQsqKqeV188cXFPscfPbPlllsC5StmygYtZ1966aUA9OzZE4DPP/+csWPHZvQe+ptIlQW4/vrrgxxmYKgo6JprrvGaLfhjjaTASZVSZJNacaZDCmZZ2l0Ggb8BhVpo9u/f39u2WgYWCvNXcwsxYsQImjRpArj5yLYTNYW8RYsW1K5dO+Wxm2++GcBrcyv8togooHNg1apVUx6Xktq1a9ciS/xqk+uPoEpGCurSpUuDHXAZ0SqFrCkqshR9+vTxbAmlNRYRBx98cBFLkv6OUW7qYBhG2TBF1TAMwzAMw4gkBSV5RwsKCjJOTZZPauDAgYALqJfSKK9cMoqP0d3vHnvsAbgIqhYtWnjeVMVTlYfCwsKU5O+yzM+PChZmzZoFlBz4f9555wGZqcvlIcj5ySs3ZcoUIKEs33777QAMGjQIKBpxI/Xu2WefBRLKqooZpFKWJ54qyPnVrVsXgEceeQSApk2bZj2udHz11Vcpn5MJQcxPLU7vuusuALp06eL9ToVP/gIoHXd+NQ/gk08+AeDVV18FoFu3bmUdkkeQ28/P/PnzvdWcjz/+GIBGjRoBCf8quHOIznnNmjXzPKBBUJ75Sc0OIi5ryZIlQMKjq/a42h/LQxDbTwqoX1EtKCjw9svbbrsNcH55Pzr/bL/99t62FVoJuvvuuwEXi5cJ/vkZhhENTFE1DMMwDMMwIklggf+qAtYdbf369QEXRzRp0iTvTlm+RyUFqIJ+5cqVgPMCSs2JMskqqv4tRVg+PlUfxwnFcino/YADDqBfv36Aq+q/5pprANcSV9tXHtW1a9d6SntUgv7lm73//vuB4JXUsFmzZg0Aw4cPB2DnnXcGEisbatern6XxwAMPeBFdYaVulIYq3evVq+c9JjVNFeEzZsxIec2oUaMAAlVTo8B1110HuPoArWxECQXw+xVVcPuq9l0/fs94Mjr3ZOqjNwwjPpiiahiGYRiGYUSSwDyq4uijjwZcKLrUm/Xr13vVnFJBVHEtj6oUgbK0eCwLufCovvDCC0CqR1U5m0OHDs327bMiFx5AqRxjx46lTZs2gNtuqgRXK1z5xeQLmzZtGh07dizvEDyCmN8+++wDwAcffJDy+KpVqzwPsb8pgeZQs2ZNIH2guvIa1aJy5MiRALz44osZjy0X208Kcp8+fbxM1ObNm6d9rirEtd8+9NBDGbcFzoRczE9K6qJFizzFTdmoe++9N+BWd5TcocQOfxpAeSnP/DT2cePGAUUbLJSEzpdKrSiLL7MsBLH95MVVIwnVNNSqVavErO3//zyNA0gcazregsiJNY+qYUQTU1QNwzAMwzCMSBK4oiqkWqi1qirfk8kkXzRIcq2oKpexe/fugOuIky9yWVUNcNpppwGuFWWdOnVSfq9WuKo0loc1KIKYn9qjvvLKK4BThwcNGlSql3iTTRKW7nSV/KpYVu5uNuR6+4VNLuYnFf+9994rtjOTjkO1YQ6isj4dQcxPvtprr70WcJ7/ZKTWDxgwQJ8LFE3hCJpc7p89e/b0qvn96RPTp08HXPKEeOSRR/jhhx+CGoIpqoYRUUxRNQzDMAzDMCJJzhTVKBKkIqAuMcrhbN68uee3kuKYb/KlyKkjjhRTeQHVX1sZukFjimO8yeX8GjZs6Cn58tWqG5q8qUF7Uv3Y9os3pqgaRjSxC9UKhM0v3tj84o3NL97YhaphRBNb+jcMwzAMwzAiiV2oGoZhGIZhGJHELlQNwzAMwzCMSGIXqoZhGIZhGEYksQtVwzAMwzAMI5KUWPVvGIZhGIZhGGFhiqphGIZhGIYRSexC1TAMwzAMw4gkdqFqGIZhGIZhRBK7UDUMwzAMwzAiiV2oGoZhGIZhGJHELlQNwzAMwzCMSPJ/d3r8T+P40YAAAAAASUVORK5CYII=\n","text/plain":["<Figure size 864x864 with 128 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"tOdQ3r5diEwr"},"source":["# Fully Connected VAE\n","\n","Our first VAE implementation will consist solely of fully connected layers. We'll take the `1 x 28 x 28` shape of our input and flatten the features to create an input dimension size of 784. In this section you'll define the Encoder and Decoder models in the VAE class of `vae.py` and implement the reparametrization trick, forward pass, and loss function to train your first VAE."]},{"cell_type":"markdown","metadata":{"id":"aqMjCTSMi0jX"},"source":["## FC-VAE Encoder\n","\n","Now lets start building our fully-connected VAE network. We'll start with the encoder, which will take our images as input (after flattening C,H,W to D shape) and pass them through a three Linear+ReLU layers. We'll use this hidden dimension representation to predict both the posterior mu and posterior log-variance using two separate linear layers (both shape (N,Z)). \n","\n","Note that we are calling this the 'logvar' layer because we'll use the log-variance (instead of variance or standard deviation) to stabilize training. This will specifically matter more when you compute reparametrization and the loss function later. \n","\n","*Define an `encoder`, `hidden_dim` (H), `mu_layer`, and `logvar_layer` in the initialization of the VAE class in `vae.py`. Use nn.Sequential to define the encoder, and separate Linear layers for the mu and logvar layers. In all of these layers, H will be a hidden dimension you set and will be the same across all encoder and decoder layers. Architecture for the encoder is described below:*\n","\n","\n"," * `Flatten` (Hint: nn.Flatten)\n"," * Fully connected layer with input size 784 (`input_size`) and output size H\n"," * `ReLU`\n"," * Fully connected layer with input_size H and output size H\n"," * `ReLU`\n"," * Fully connected layer with input_size H and output size H\n"," * `ReLU`\n"]},{"cell_type":"markdown","metadata":{"id":"gTuEAFrgkTyt"},"source":["## FC-VAE Decoder\n","\n","We'll now define the decoder, which will take the latent space representation and generate a reconstructed image. The architecture is as follows: \n","\n"," * Fully connected layer with input size as the latent size (Z) and output size H\n"," * `ReLU`\n"," * Fully connected layer with input_size H and output size H\n"," * `ReLU`\n"," * Fully connected layer with input_size H and output size H\n"," * `ReLU`\n"," * Fully connected layer with input_size H and output size 784 (`input_size`)\n"," * `Sigmoid`\n"," * `Unflatten` (nn.Unflatten)\n","\n","*Define a `decoder` in the initialization of the VAE class in `vae.py`. Like the encoding step, use `nn.Sequential`*  \n","\n"]},{"cell_type":"markdown","metadata":{"id":"aFTb-35TiOZl"},"source":["## Reparametrization \n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"SdD23s3Vf70-"},"source":["Now we'll apply a reparametrization trick in order to estimate the posterior $z$ during our forward pass, given the $\\mu$ and $\\sigma^2$ estimated by the encoder. A simple way to do this could be to simply generate a normal distribution centered at our  $\\mu$ and having a std corresponding to our $\\sigma^2$. However, we would have to backpropogate through this random sampling that is not differentiable. Instead, we sample initial random data $\\epsilon$ from a fixed distrubtion, and compute $z$ as a function of ($\\epsilon$, $\\sigma^2$, $\\mu$). Specifically:\n","\n","$z = \\mu + \\sigma\\epsilon$\n","\n","We can easily find the partial derivatives w.r.t $\\mu$ and $\\sigma^2$ and backpropagate through $z$. If $\\epsilon = \\mathcal{N} (0,1)$, then its easy to verify that the result of our forward pass calculation will be a distribution centered at $\\mu$ with variance $\\sigma^2$.\n","\n","Implement `reparametrization` in `vae.py` and verify your mean and std error are at or less than `1e-4`."]},{"cell_type":"code","metadata":{"id":"T236XnbhbVH4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615483936863,"user_tz":-60,"elapsed":1077,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"a06e1637-9b2c-4381-d0b5-4c5f1e5ec652"},"source":["reset_seed(0)\n","from vae import reparametrize\n","latent_size = 15\n","size = (1, latent_size)\n","mu = torch.zeros(size)\n","logvar = torch.ones(size)\n","\n","z = reparametrize(mu, logvar)\n","\n","expected_mean = torch.FloatTensor([-0.4363])\n","expected_std = torch.FloatTensor([1.6860])\n","z_mean = torch.mean(z, dim=-1)\n","z_std = torch.std(z, dim=-1)\n","assert z.size() == size\n","\n","print('Mean Error', rel_error(z_mean, expected_mean))\n","print('Std Error', rel_error(z_std, expected_std))"],"execution_count":23,"outputs":[{"output_type":"stream","text":["Mean Error 5.639056398351415e-05\n","Std Error 7.1412955526273885e-06\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"XOfC7oDrkUhl"},"source":["## FC-VAE Forward\n","\n","Complete the VAE class by writing the forward pass. The forward pass should pass the input image through the encoder to calculate the estimation of mu and logvar, reparametrize to estimate the latent space z, and finally pass z into the decoder to generate an image.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"IQ0UXWyMi9Gu"},"source":["## Loss Function\n","\n","Before we're able to train our final model, we'll need to define our loss function. As seen below, the loss function for VAEs contains two terms: A reconstruction loss term (left) and KL divergence term (right). \n","\n","$-E_{Z~q_{\\phi}(z|x)}[log p_{\\theta}(x|z)] + D_{KL}(q_{\\phi}(z|x), p(z)))$\n","\n","Note that this is the negative of the variational lowerbound shown in lecture--this ensures that when we are minimizing this loss term, we're maximizing the variational lowerbound. The reconstruction loss term can be computed by simply using the binary cross entropy loss between the original input pixels and the output pixels of our decoder (Hint: `nn.functional.binary_cross_entropy`). The KL divergence term works to force the latent space distribution to be close to a prior distribution (we're using a standard normal gaussian as our prior).\n","\n","To help you out, we've derived an unvectorized form of the KL divergence term for you.\n","Suppose that $q_\\phi(z|x)$ is a $Z$-dimensional diagonal Gaussian with mean $\\mu_{z|x}$ of shape $(Z,)$ and standard deviation $\\sigma_{z|x}$ of shape $(Z,)$, and that $p(z)$ is a $Z$-dimensional Gaussian with zero mean and unit variance. Then we can write the KL divergence term as:\n","\n","$D_{KL}(q_{\\phi}(z|x), p(z))) = -\\frac{1}{2} \\sum_{j=1}^{J} (1 + log(\\sigma_{z|x}^2)_{j} - (\\mu_{z|x})^2_{j} - (\\sigma_{z|x})^2_{j}$)\n","\n","It's up to you to implement a vectorized version of this loss that also operates on minibatches.\n","You should average the loss across samples in the minibatch.\n","\n","Implement `loss_function` in `vae.py` and verify your implementation below. Your relative error should be less than or equal to `1e-5`\n","\n"]},{"cell_type":"code","metadata":{"id":"vF2ZUj2FjrFa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615483940223,"user_tz":-60,"elapsed":682,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"6d696e09-0be6-4832-b411-63a22ceea74c"},"source":["from vae import loss_function\n","size = (1,15)\n","\n","image = torch.sigmoid(torch.FloatTensor([[2,5], [6,7]]).unsqueeze(0).unsqueeze(0))\n","image_hat = torch.sigmoid(torch.FloatTensor([[1,10], [9,3]]).unsqueeze(0).unsqueeze(0))\n","\n","expected_out = torch.tensor(8.5079)\n","mu, logvar = torch.ones(size), torch.zeros(size)\n","out = loss_function(image, image_hat, mu, logvar)\n","print('Loss error', rel_error(expected_out,out))\n"],"execution_count":24,"outputs":[{"output_type":"stream","text":["Loss error 2.1297676389877955e-06\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"wV8fbzenkAXm"},"source":["\n","## Train a model\n","\n","Now that we have our VAE defined and loss function ready, lets train our model! Our training script is provided  in `a6_helper.py`, and we have pre-defined an Adam optimizer, learning rate, and # of epochs for you to use. \n","\n","Training for 10 epochs should take ~2 minutes and your loss should be less than 120."]},{"cell_type":"code","metadata":{"id":"rWaaacNHsfao","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615417916757,"user_tz":-60,"elapsed":56502,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"444b6883-7d8c-42d2-dbfa-208fd0f9436b"},"source":["num_epochs = 10\n","latent_size = 15\n","from vae import VAE\n","from a6_helper import train_vae\n","input_size = 28*28\n","device = 'cuda'\n","vae_model = VAE(input_size, latent_size=latent_size)\n","vae_model.cuda()\n","for epoch in range(0, num_epochs):\n","  train_vae(epoch, vae_model, loader_train)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Train Epoch: 0 \tLoss: 163.427429\n","Train Epoch: 1 \tLoss: 138.039627\n","Train Epoch: 2 \tLoss: 123.292709\n","Train Epoch: 3 \tLoss: 121.097710\n","Train Epoch: 4 \tLoss: 125.457184\n","Train Epoch: 5 \tLoss: 114.404221\n","Train Epoch: 6 \tLoss: 116.560722\n","Train Epoch: 7 \tLoss: 119.169411\n","Train Epoch: 8 \tLoss: 112.095634\n","Train Epoch: 9 \tLoss: 109.473122\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"JT6Ek-26jjJD"},"source":["## Visualize results\n","\n","After training our VAE network, we're able to take advantage of its power to generate new training examples. This process simply involves the decoder: we intialize some random distribution for our latent spaces z, and generate new examples by passing these latent space into the decoder. \n","\n","Run the cell below to generate new images! You should be able to visually recognize many of the digits, although some may be a bit blurry or badly formed. Our next model will see improvement in these results. "]},{"cell_type":"code","metadata":{"id":"RhhrsgrMTyTi","colab":{"base_uri":"https://localhost:8080/","height":84},"executionInfo":{"status":"ok","timestamp":1615417927328,"user_tz":-60,"elapsed":1034,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"6ee80a0c-64a0-44e2-a8f9-a10e065737c1"},"source":["z = torch.randn(10, latent_size).to(device='cuda')\n","import matplotlib.gridspec as gridspec\n","vae_model.eval()\n","samples = vae_model.decoder(z).data.cpu().numpy()\n","\n","fig = plt.figure(figsize=(10, 1))\n","gspec = gridspec.GridSpec(1, 10)\n","gspec.update(wspace=0.05, hspace=0.05)\n","for i, sample in enumerate(samples):\n","  ax = plt.subplot(gspec[i])\n","  plt.axis('off')\n","  ax.set_xticklabels([])\n","  ax.set_yticklabels([])\n","  ax.set_aspect('equal')\n","  plt.imshow(sample.reshape(28,28), cmap='Greys_r')\n","  plt.savefig(os.path.join(GOOGLE_DRIVE_PATH,'vae_generation.jpg'))"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x72 with 10 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"sx3HGSpXk1MY"},"source":["## Latent Space Interpolation\n","\n","As a final visual test of our trained VAE model, we can perform interpolation in latent space. We generate random latent vectors $z_0$ and $z_1$, and linearly interplate between them; we run each interpolated vector through the trained generator to produce an image.\n","\n","Each row of the figure below interpolates between two random vectors. For the most part the model should exhibit smooth transitions along each row, demonstrating that the model has learned something nontrivial about the underlying spatial structure of the digits it is modeling."]},{"cell_type":"code","metadata":{"id":"XZ_4XsFURmN1","colab":{"base_uri":"https://localhost:8080/","height":630},"executionInfo":{"status":"ok","timestamp":1615417976922,"user_tz":-60,"elapsed":4771,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"b1c2e540-890e-488e-fb59-cc1b771af454"},"source":["S = 12\n","latent_size = 15\n","device = 'cuda'\n","z0 = torch.randn(S,latent_size , device=device)\n","z1 = torch.randn(S, latent_size, device=device)\n","w = torch.linspace(0, 1, S, device=device).view(S, 1, 1)\n","z = (w * z0 + (1 - w) * z1).transpose(0, 1).reshape(S * S, latent_size)\n","x = vae_model.decoder(z)\n","show_images(x.data.cpu())"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x864 with 144 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"wzS_ufzEkhah"},"source":["# Conditional FC-VAE \n","\n","The second model you'll develop will be very similar to the FC-VAE, but with a slight conditional twist to it. We'll use what we know about the labels of each MNIST image, and *condition* our latent space and image generation on the specific class. Instead of $q_{\\phi} (z|x)$ and $p_{\\phi}(x|z)$ we have $q_{\\phi} (z|x,c)$  and $p_{\\phi}(x|z, c)$\n","\n","This will allow us to do some powerful conditional generation at inference time. We can specifically choose to generate more 1s, 2s, 9s, etc. instead of simply generating new digits randomly."]},{"cell_type":"markdown","metadata":{"id":"hle0JuhwklKc"},"source":["## Define Network with class input\n","\n","Our CVAE architecture will be the same as our FC-VAE architecture, except we'll now add a one-hot label vector to both the x input (in our case, the flattened image dimensions) and the z latent space. \n","\n","If our one-hot vector is called `c`, then `c[label] = 1` and `c = 0` elsewhere.\n","\n","For the `CVAE` class in `vae.py` use the same FC-VAE architecture implemented in the last network with the following modifications:\n","\n","1. Modify the first linear layer of your `encoder` to take in not only the flattened input image, but also the one-hot label vector `c`\n","2. Modify the first layer of your `decoder` to project the latent space + one-hot vector to the `hidden_dim`\n","3. Lastly, implement the `forward` pass to combine the flattened input image with the one-hot vectors (`torch.cat`) before passing them to the `encoder` and combine the latent space with the one-hot vectors (`torch.cat`) before passing them to the `decoder`"]},{"cell_type":"markdown","metadata":{"id":"bUzKyFI9kp8i"},"source":["## Train model\n","\n","Using the same training script, let's now train our CVAE! \n","\n","Training for 10 epochs should take ~2 minutes and your loss should be less than 120."]},{"cell_type":"code","metadata":{"id":"N1dzKDUsunbD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615485876402,"user_tz":-60,"elapsed":89452,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"16b991f6-8c91-4399-963c-0c8abe3ba9b5"},"source":["from vae import CVAE\n","num_epochs = 10\n","latent_size = 15\n","from a6_helper import train_vae\n","input_size = 28*28\n","device = 'cuda'\n","\n","cvae = CVAE(input_size, latent_size=latent_size)\n","cvae.cuda()\n","for epoch in range(0, num_epochs):\n","  train_vae(epoch, cvae, loader_train, cond=True)"],"execution_count":32,"outputs":[{"output_type":"stream","text":["Train Epoch: 0 \tLoss: 143.905441\n","Train Epoch: 1 \tLoss: 131.232330\n","Train Epoch: 2 \tLoss: 126.080849\n","Train Epoch: 3 \tLoss: 119.384857\n","Train Epoch: 4 \tLoss: 111.767593\n","Train Epoch: 5 \tLoss: 111.876450\n","Train Epoch: 6 \tLoss: 119.063759\n","Train Epoch: 7 \tLoss: 110.267746\n","Train Epoch: 8 \tLoss: 109.231987\n","Train Epoch: 9 \tLoss: 102.071381\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"GMAyFBZTkr1Y"},"source":["## Visualize Results\n","\n","We've trained our CVAE, now lets conditionally generate some new data! This time, we can specify the class we want to generate by adding our one hot matrix of class labels. We use `torch.eye` to create an identity matrix, gives effectively gives us one label for each digit. When you run the cell below, you should get one example per digit. Each digit should be reasonably distinguishable (it is ok to run this cell a few times to save your best results).\n","\n"]},{"cell_type":"code","metadata":{"id":"GCfwpz0NALdZ","colab":{"base_uri":"https://localhost:8080/","height":84},"executionInfo":{"status":"ok","timestamp":1615485965142,"user_tz":-60,"elapsed":1239,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"c2f9acee-16e1-44af-c98e-13291349a4d2"},"source":["z = torch.randn(10, latent_size)\n","c = torch.eye(10, 10) # [one hot labels for 0-9]\n","import matplotlib.gridspec as gridspec\n","z = torch.cat((z,c), dim=-1).to(device='cuda')\n","cvae.eval()\n","samples = cvae.decoder(z).data.cpu().numpy()\n","\n","fig = plt.figure(figsize=(10, 1))\n","gspec = gridspec.GridSpec(1, 10)\n","gspec.update(wspace=0.05, hspace=0.05)\n","for i, sample in enumerate(samples):\n","  ax = plt.subplot(gspec[i])\n","  plt.axis('off')\n","  ax.set_xticklabels([])\n","  ax.set_yticklabels([])\n","  ax.set_aspect('equal')\n","  plt.imshow(sample.reshape(28, 28), cmap='Greys_r')\n","  plt.savefig(os.path.join(GOOGLE_DRIVE_PATH,'conditional_vae_generation.jpg'))"],"execution_count":36,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjwAAABDCAYAAACY5N+nAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAe2UlEQVR4nO2dfbDVVfXGP4cLXISrXIOkVExNSntTTNTKLCDNVKbS0LQGzV6spqYmp+mPrEmzmWqmaUqUakbNShMhM4rCEipT097MXhBTwkwkE0LiRRS4/P64v8/e5+7rCS6cN47r+ec7XM49d++91t7fvZ79rLUr27dvJxAIBAKBQKCTMazVDQgEAoFAIBBoNGLDEwgEAoFAoOMRG55AIBAIBAIdj9jwBAKBQCAQ6HjEhicQCAQCgUDHY/j/+s9KpdKRKVzbt2+vQPRvT0X0b8+G/YPO72P0b89E9G/PRvUaU41geAKBQCAQCHQ8YsMTCAQCgUCg4xEbnkAgEAgEAh2P2PAEAoFAIBDoeMSGJxAIBAKBQMfjf2ZpNRJdXV0ADBvWv+caNWoUAFu3bgVgy5YtAGzbtg2ATrvzq1IZKCLvlP7Zr1r965R+PttQ2hPClq2GNnEN3WuvvQCYMGECAC996UuBvJa65m7YsAGAFStWsHr1agA2b94M5PU20DrEGto4BMMTCAQCgUCg49E0hmfEiBEAHH300QCcccYZAJx88skA7LvvvkBmeO655x4A5s6dC8DixYsBeOKJJ4A9Z5drv1//+tcD8JnPfAaAjRs3AnDhhRcC8NBDDwF7Tr+MPuzf85//fACOPPJIINv5scceA+D+++8f8Pz3v/+dbN2KPhsVG/U+97nPBWCfffYBYP/99wfgxS9+MQATJ04E4LDDDgNyFP3oo48C8Mgjjwx4/ulPf2Lp0qUArF27FoCnn366Ud3ZbZTRpOPjz2UPRo4cmZ6bNm0CMmPQaexApVKpGW339fUBrfFd2zJ69GgAXvSiFwF5zh133HFA9l3tIovz29/+FoADDzyQX//610D246eeegrI/dsT8UxsZIlW2s01Z++99wbggAMOALIdnWu2cfny5UBeS//1r38B/cxdp825RiMYnkAgEAgEAh2PhjM8JQMg03HaaacB8IIXvGDA54ws3OW6C16zZg0Ad911FwBPPvlko5teF9gfGYNJkyYBsH79egDGjRsH9J+ntzPKqNIo8rzzzhvw7+c85zkAdHd3AzlKsb9GlLNnz+bee+8d8H/NiCqNrvQvmZ2pU6cCmXF87WtfC8DYsWMH/J5P+2Wb1UnIeqxcuZLbbrsNgKuvvhrIkZqfaQc4DvbTeViyb+PHjwcy0zV69Og0F2V4WoUych4+fPiApz/3c/bNp2yW7JW+O2rUqGRfv0usW7duwLOZkbZtOeKIIwD4xCc+AWSGxzVTn/zvf/8L9LOqMHBt9bPlfJXpaSQT4rjv6OelPfVRP+e7QHtqs2HDhg1aU/QBx6aZsN0HH3wwAG9961sBOPPMM4F+xg0yE6y9ZN/mzZsHwO233w70Mz7B8AwNwfAEAoFAIBDoeDSM4XH3vd9++wFw0kknAXDOOecAeZdbMjvu0mUSXvaylwHwzne+E4B//OMfQD8jsifoXeyXzIf9cnyMvtoVZs8Z2X/4wx8G4B3veAeQo0n747NkQIwg3/jGNwJw6KGH8sUvfhGAH/3oR0CO1Bpl10qlkvxtzJgxAEyePBnIjE4ZJRtV7ihjwkjL3zv88MOT73v2fs011wCN7+fOwH4dfvjhAEyZMgXIbJuMo/Z7+ctfDmSdwcaNG7njjjsGfKZZKBmdnp4eINvUuWbf/Lm6OX/PNeiggw4a8G+ZnnHjxiUmwHH5+9//DmQdzE033QRkBroZjMjznvc8gDR/XvWqVwHZpq6hq1atAuDOO+8E4Oc//zmQNSCQ159aGq7dZRDKTLJqXZTtVTennWRdtaPsov/vHFNHp43s98qVK4F+e9tX3xvqmPxMMxgS/c1+zJo1C8j6Tf338ccfH9BW/fAVr3jFgP+XKX7iiSeawsSVKPWb6m8dS/srU+XT/6/FrjVDF9ewDY8vyqOOOgqAmTNnAv0vOsiDIr3v5JTGk97zezT69OnTgf6Xhw6+J8CNjk7iS7DdjrJ0Zsfd1FZpc498XHxq0dLl95XHBpMmTeKCCy4A8svDid7IDU85qXwJ3nfffUCevC4kbtQUy/vUX6Wbncz2afz48WmBe9vb3gbAggULgOzjrYRHWG503NDcfffdQG6j89RxeOELXwjAN7/5zXSc0+yNm77kC9E15TWveQ0Ap5xyCpCP0W278Pc82ilLZFRv1vUX/cFNkYHYf/7zHwB+8IMfAI09KnFjcOmllwJ5o+Ocsq2+GG+88UYAfvzjHwN5rXHD0NPTk9YlbVnrCGhXbVz+XqVSSX/Tp2u7x/3HHHMMkI/stNfDDz8M5LmnHMK1yJ/r08uXL0//Z9/1b9Pxm3G8rH+95CUvAfJ6IGbPng3A9ddfD0Bvby+Q350Gia4nBlL33XffbttnqOjq6krtsH0GwwYYJ5xwApA3pG4yHfMlS5YA/YkdkNd//dbPN6JPcaQVCAQCgUCg41F3hscoSVryDW94A5B33TIcUqnf+973APjlL38J5F2hjJC7eGlcd7vz589P6b7tDHfg06ZNAzKN63FAO7FUlUoltU87KC7XfqW40YhQhsOnNKb/79OIa+TIkSkiMEKQ4WkUqiN2hbYKp2Vq/vnPf6b2VX9OBsh/G/H7tA9nnXUW0B+FlVS2bFIrj7L0R0X0MniyHUZhHrvpBzIa9v+ee+5JkVgzMWzYsHQEYNr1u9/9biAfm8te6YvaSHvIXmoHfbW0y/r16xMD4FrjUYhR6bJly4DGMju29z3veQ+QGQLXUvupD1977bUA3HLLLUBmN/weGZ6JEycmG+oXDzzwAFD/dan6uEM7yKbqWz5lfDxqtG3a3XeCbdRGMgQyR0cddVR6H8lg6td//vOfB/y7EXOyLOmgf2oH7aO9ZKg8HnXdqD6ChNaUf7AvEyZMSKcsHv/79FRGOPau+c5L7euc+cY3vgFkpsvTj0b0MxieQCAQCAQCHY+6MzwyBO72TL1z122U6/nyFVdcAWTNgLtAoxVFsoolPQedNGlSirLaWbzseKh9EKZnt1Pbq8/XjbZkLkoRo0yJEaLRif2ywKDRzamnngrkwoRdXV1Jk+DYKK5spAi2LMJmlChzYQRvRCM7UKYyl8I9xc/qRqpTYhW8qpNoJWzvm9/8ZiDrXSz0aQFMWQ91Ivbv5ptvBvqj0Fb47vDhw5NPnnvuuUD2rTIhwHGXOZSlMWJ+8MEHgawNMeLUN9asWZPYA9ctGQEZav9GIyATYkR90UUXAZkh0L9kJV1TtaW+KktnpC1bu88++6S+/+1vfwN2rmjfrqBa61eWB1AX578Vhpdp9L///e+BbC/tLbOjrVxzZ82alTRXjqXzvxmJAyUzZdFS33H6n36p3zlH1aa5ZnoqoO81s5CpYzpx4kTe/va3D2if7bB9v/rVr4C83ml75+3ZZ58N5PGYMWMGAAsXLgQyyxwMTyAQCAQCgcAuoO4Mj7txz5lV2BuNqMyeM2cOkFPs/H93r0ZU7m7dTbqrP+aYY/jDH/4AtJcOpoSaF8+rhW1vJwwbNixFj7bbqEPGzSjYqMQoxQwko0uZETMuZPqqM2J2lOHVCJTp5GWxMqMVo6wy4i2L051++ukAXHbZZcDAsvB+l5F3qwv0QY6qPvrRjwI5ctNuMlzqPLwCRjZu0aJFQLZvs6Aduru7U/bOscceC2SbGBHKeJidJHOoDktGQd/2WerNqqP/Whc3NiIKta9qWD71qU8BuUipMEPMrBf1N641akDUa2lD2duNGzemsXNsjK7rbd/qVHR9Th9zzXeNkYGzbTI7MvrlnBXa8ZBDDgH6mSDLTcjo+GxmKQV9RvvZfu3nmmt/tduJJ5444Pe0jSxstW6sUdlarte2YerUqen0xvaqwbHsgYyqY11qGYVrkHpfWWTXoi1bttS9P8HwBAKBQCAQ6HjUleGpVCopc0JFupGyyvMrr7wS2HE2gGfjf/zjHwf83Ojl+OOPTyX725nhcVfrLt6I0N16O6C6Vo5RgxGf5/5lVpLn6EZlRmFGWe7+1Q+U9Ze2b9+eftcdfSuKZ/m0vTI09ltoPzPLZDDVwHguLzZs2MBVV10FwOWXXw601k/t13XXXQdk5tX59Z3vfAfIbTz++OOBnAmlfRudSVcLsoGHHXZYynjUt4zWbdv8+fMB0tUerjWlbqtk9Ur/2759e01mp5GQibG4p1ktzh3ZbzON1MvJ3BiB+7TfsuNqXXp7e9N3yjY4r8Xu9ru8pHfkyJFpLrk22k61Lq4LXqGg/WpdQWMby2tfpkyZkn7m+6dRWWjPhPKajjKDVX3cX/7yFyDbXf+2LpHjYQ2hZypy2Sj/dF2UKZsxY0bSKXpaY+FY51+ZsagN9EPt79rrWitje8MNNwCNuT4qGJ5AIBAIBAIdj7oyPN3d3Umr4S7Q3bg767L+TK2dqbtioxjPda1Cue+++6adYzvDSzU9tzaSaodqu6Kshgx5N65Gp7xiwVoJnieXUaSKfKNUf+7vb968OWks1Fw0E2WNDP3VLECzAr3s9oADDgBypd2yP0Zvjstll12Waky1MjvL9p1//vlAro5qlP/pT38ayP5o1P3+978fyDoLs++MTqu1Tc3IdKmurWJkLCNgJKhGSltoUyNn266fq0PT//wefblaQ1DrapF6o1KpJN3iBz7wAWBwpeiy7o5zTd+1To3jYdaTc7k60tavZVn8twx0vbUu1cxKebWEWh61OrZ3Rxe0liytlX4PPPDAZGuvdZENkyVrJMpq7uoc1WZZG8jacuJ1r3sdkBmTn/3sZ0CuGSaDUs1A1htlZXzn0ogRI9J7V72f77TqdsHg94paHVly7akfuOY612Wy6tqvun9jIBAIBAKBQJuhrgxPb2/voIvsjKrc3ZohsSO4Syyztvz5wQcfnM4Cm7FbHyrc3b761a8G8i7Xc+lm3OGys7BtY8eOTVGzUZcVro0y1WSpn3Dsy4sbrZ9kFFPW3li5ciWLFy8GmjsW5cWFRh2eH1sZ3HuZjGz0tTKzTP822v7Sl74E9N+Lo++2staS2RWf/exngTz+3/3ud4HM3BhVGW3KTMoGyILIiMHgSxsbWa1WZveII45IPqkv+nf1SXVVshT6pD4sk+N32zeZhZ/85CdAP6tcXs7YaFt2d3enOie2W59znB955BEgMx+yjtraNdbq9eqvZB/12SlTpqQquf4N579R/O5WkC6ZsUqlMkjXo25IPZk1vWppdkqUl6p6QXV3d3eqMm1tmGZc8lrC8Xa9sw6S70orhbuGmrkss3PXXXcBg/22kQxPqW/TJtu2bUt/XzbQzE9PY/QZ+yPzqBZNfy3fKb5zfFYqlcjSCgQCgUAgEBgq6q7hMVvFXZs7Q2thDPU6e3eaRnju5kePHp3+RjvCtqkBEeo6mlkHohYcS6P2Qw45JNU4kdnwbN9dt1F0WbVWvYDMyCtf+UognwFrb6OUBx98cJB255luq64nKpVKiiq1j/5qv4yS7W+pEytrtBgBGZXJmGzatKmlzI4M1uc//3kg90dGTl2H0ZY6kI997GNA9onyHh/nYTU8i28kwyPDNnz48LSGGEFqEz+jLe2TsH1lJp4RqL7vz+fOnTuoLs0z1eipJyZMmJDYNeeOsA22SS2IkbZzUGZg6dKlQM64lK2T1ezp6UljpHbLsalXxWXnj/7Y29ub5p6Mk76or8n4Ota12uJ6oU9615hr1urVq1PGnvOzmdWJhf2QmVO7aIV56yRpP+2mhqfUzTVyvpXQ5373u98B/SyjtpQV//rXvz6g3cIMT9tdMsIy4K5N+qD+vGzZsrq/A4LhCQQCgUAg0PGoK8MzfPjwdAbs7tNdqmepQ92VltkwPru6ugZlL7QTPH83gipv9m0HuFNXt3H66aenKqWOu3WPjHqNqtz5y9hod/tdRqdl/1esWJG+26jS7yxvWt9d2OZKpTLIj9RBeLZv1pl+avQh82N0alRpG+1XtQaikTdo14J9NRvrTW96EzCYmTCb0voaRlVmSvg5Wbiydk1fX19DtTui1BIsW7YsVXS17kxZh8XPahN9VK2IWg/ZLX1bn/UG+enTpyfNnWxEo+7O0h8PPfTQNC/slxG9/bG9Mjz+3HouttXMu7KeibY+4YQTkl/bT3+nXhWkS/Zty5YtyW+ce7a/lj9Vz1/I65aMwHnnnQfAzJkzgdzfBQsWJD2WbEIrYf8cW9cHx1zWXJtoX+dgo5jv/wX/lj41Z86clMFpNqH1gmRIy3sXta+asl/84hdAXksvvvhiIPu1NdtuvfXWuvcnGJ5AIBAIBAIdj7oyPFu2bEmRr7tZmR3PJ4caDXoe727eXf7q1avb6qbxElaqlYWSvfAsuZVtdwzN0PjIRz4C9Kvt3Z2XdRiqmTXI/VIDI0tT1utxl+/TcRg+fHiqx+B3WXdDjcKuMghlFogR8/r16wdFzf4tI0G1ZuXtyrZRxu5d73oXkLVKnlfbp1bclTZs2LBkD9kPdQPC8S9vZJb1cOxkvIz81VcYKW/evLkpWoIyqn344YdTBK9WxVpB5c32RsbWCzGC1raymWaZWP9ETcUpp5ySxlMtSKkvqReq54tzyb9hf7SJ/XVuluykN5+XrKO+KRNy5JFHDmK/HKN6VSK2X7Z53bp1idkoWST7q32q76WD3F999sILLwSy3WSMfvjDHwL9ekn93/dPK7WT5e3psl6uQWZjyeyY1SRjWd4F10yo/1q4cGHKxvL9YX/2228/INtJNlRdo1otfc55JmNkv8t7J+uJYHgCgUAgEAh0POrK8Gzbtm0QQ+Au1l37zt7q6u7euiju+vzexx57LFV4bCdUn8XD4HoWZq21EupsPvShDwE5S6WnpydFQKWeRpTMT8nolPVKSu2Idj366KPTWfWyZcuAzBb95je/AXKEOlQGQV/xpnbbuHz5ch5//PEB320/tY8MRtlu7ejntaPRjX/DKLpeWS47g2ptg2NoFOgdWeXt756Xe9u7DI9ZXNdffz2Qswodt+r7qJoRLZeaq7Vr16bI0fmvzWQMypuxazGFshkyI46dtbN6e3uTnxilqmWoN6orkJe2KueOc9BxsAaUjJa+6DwwkpaVlDkYNWpU+h31Ffr17tq21NvoV0899VTNirxlXRbfHfbbuXXmmWcC+T47GZJvfetbQK75tmrVqppsUitgP2SFzZZbuHAhkOeq+rv3ve99QB6PRYsWAZm1beYpgf6wbt26pBX761//OuAzO1uNvGTgS61jeat6PREMTyAQCAQCgY5HXRmeTZs2pbNStTdW2nU3W1ZMLuFu1ijLc1p/7k59yZIlbZXxJNy9quFx1+sOuRE3wA4Vahaso2C039XVlexiP4zQhP/2WdbOkREqNQDlfTfjx49P7EiZpeUZ8VDh37B/H/zgB4GBZ+Te8GtU77l/GXXWarfsmBkJZrPZ39JPm4FqNk0mxsqytqO8O8zzcutEaU+ZjK997WtAHif9V3v39fU1JcI0CvQ5ZsyYQTqsktEps/xq2dSfyySYnWUdlxEjRtSs5VNv2NY1a9akeVCyJCV7J8PlOFjXxawZWTsZA+eF/d20aVOyt08ZvN1FyfBXszm1WH7nju8KNSHejXXqqacCOTvIeX3ttdcCcOONNwL1zzTbXdhf55yaI99fttu1SU2WDJa/5x1p3qnVKj3S7lYd9/fsf1kbTb1XI1jyYHgCgUAgEAh0POrK8Dz55JPpPFmGx0wI74e54oorgKyaLzUF3oPj3T+ePxsxGMHOmzevLaoVl5AJMGvH3WuZedQKONa1dDnV0ZdRZam9Knfdfpd6ArM91FX43Uaf1Zouv7vUA+1ufRcjWzOVZGEmT57MTTfdBMDNN98MDK6gW9YNUQeh7uEtb3nLgO+2fzJF6o9aEV1u3bo1/d3yfjnt5s9lM4z6tePs2bOBnFFR9qNkjJoF/+64ceOS5sxI2DXHu5jU2ZS2FdpMDdmMGTOAXI+qmrVTryDT1Si76utr165N2gi1DM4LGdHy7iFZKO9mKmtI+XnnmyzOvffey7e//W0gz996M1jV2VkwcI1x3ttOWXHXCllU55qMj1qd6667LvUDhl7Fv1lw3O2fDNaSJUuAfNeZdjGrUNbtggsuAPIaZEZpK+p81QPax/arpRPWiSrvLawHguEJBAKBQCDQ8agrw/P0008zd+5cIJ8bqxm46KKLgFzJ9ZZbbgEyEzRt2jQATjzxRCBHM0YDns/L/JT1RdoFMlpGZ+5mrQ7b7Mj4mWCUL1umZgEGa3J8lpWSPSe33oy6m7LuRZn5YqbFmDFj0g5fNsEoelerojq2RrT6nm3o6enh3HPPHdBPoyuZKaPJ8oZfmRAZRzNKjPitIXTbbbcBrYu+dhTdGlWb/ejYaEdr2+yIyWhWFF364fbt21MGpFWk9WOZkfvvvx/It5/LXsg2aNvzzz8fyJG3YyEjtGzZspStJpvUaFZ5w4YNzJs3D8i+51pSrWOqfpb3m5UaJ5/2QUbk8ssvTwxWvZkrx0nbVFdeL+8yM5vy5JNPBgbfw+d69bnPfQ7I2Uoyee2i1akF+yEDZ/9/+tOfAoP1gz5dB2UcHa9Ogayya6d7Bt/9PT09dc/EruuGp6+vLy0QXnkvLeli4tGWqYW+eEraVaP74rzkkksAuOaaa9LfakfYXzdoLjKKSNthw6MT+XKWYp0wYcKgo52SZnWx8TI5Fx2fTt7yYj8XrQceeADo3zj4InLDY7t294JG/4ZFL51I3d3d6WX58Y9/HMh20i4uLqVYu1yMpM8VGuqfFuhrN1rd/nmdgunoHjV8+ctfBtrr6hPIvqBQd+nSpemY0aMPC6D51K98+oJxIfUoS1vrq/qoxUEvueSSZN9mHUVv3bo1BYNuss855xwgH+mUQu5yTXEOOmZe7+ML1jX6oYceSn5cbzimBgauA6NHj04/8+j57LPPBmDKlClAXktM1y4vAK2VYNCucCw8qimPdMpreBwrN37OAfvfru++XYX9ci1yrzBmzJh03FUvW8eRViAQCAQCgY5HXRkeyIyG1xVcffXVQD4KkMkpL/4sd70WxLr00ksBuOGGG4DBgtt2gdGWAkKjLgWVJfOxswUY64mSKr3yyiuBHAkee+yxSazr8YBHV/7b8ubSyEa+2qW8UkLGwOjZaHPs2LFJrFgeC+3qBY3V1w9AP2UP8N73vhfop86N9vW/WpfTivKiPyl6xclf+cpXgHw01q5CQvt92mmnAVmU7ZGGxz/tFjXbHpm4lStXphL82lC2yrln6QDTeP1cWSKiPFJ1rVLY/uijjya/bta49PX1JTb1q1/9KpCZylmzZgFZBlBeMSEbe/fddwPw/e9/H8hi7vKan0aWFiiFyaZW77///ollND1bey1evBjIDJTHrLtagLRdoF1kqiwbcNZZZw34f/vp0Z7p+K6HztV2XWOGCuehR9D2U4bnoIMOSu/Peh1bBsMTCAQCgUCg41F3hsedmKXK3cUrOJs+fTqQz9OF0f2cOXOAnDZsOne7n1vK3BgRGjG7e1VX0g4COyMEz8pNTZ0/f36KyIw2/GxZ3K3WxZFl4UIjPb9PBnCvvfZKWgwZprII4K7C71FvZMQ8bdo0TjrpJCBrWfTDUoSsnkhmS12FqaJGy+Vli+0WhTr+6gLUvTjGMnb1KjjXKFSn28vIGP3deuutQC4FIZsgm6X2RX9Xf6ZtZevUX6lradVc9e/av6uuugrIjI1z1KcspWxsLZ9sxVUElhmZPHky0K9jMXHFz5iebQE+Gal2ZfOHCu3ppaYmcOifMpSOR1lCQu2V7Hi7vwt3Fvqja095aezUqVMTy1erxMRQEQxPIBAIBAKBjkflf+36K5VK3UOCWllKTdayVP6/LXX/o+Vlmu7um7krb2T/dhY7ykarLio11Kysne1fqcvp6upK9ikZHdtQapPKtjXDT+tpP/uuvmXmzJlAjqZkYmU9msFq2L//b9+Q+1iyh2Vp+lIfKMprT8psn3rO0XaYg43EjvqnLT75yU8CcMYZZwD9mVnOQVlvMyZlxdvh+p1G2E9G7rjjjgMyw2N2muUH9EsZahmwO++8E6gPG9sO/un8tUTGxRdfDGQ2etGiRXzhC18ABhcn3NE6XL3GVCMYnkAgEAgEAh2Pumt4doR20zjUG628OqKdsCM7N4lJGPDs6+sbpEnqdNj3VatWAfmiRcfBjL09aV6WTEyZSVfWEtqT+tYp0BZeQms0v/fee6cMwQULFgCDr1boVMgw3n777QDccccdwOBadI5Vq7VkjYb9tn+uRdaFW7FixYCriGD336/B8AQCgUAgEOh4NJ3hCQQCzYcRt1mPnYhgctoPZr5Zr2rMmDGJgZNlldl5ttjvmZhnePadDth/s2plwMyq7e3tTT+LOjyBQCAQCAQCO4mmZ2m1A9pBod5IRP/2bDxb+ged38fo356J6F/zoGbJ7EqZrm3btu0y6xdZWoFAIBAIBJ61+J8MTyAQCAQCgUAnIBieQCAQCAQCHY/Y8AQCgUAgEOh4xIYnEAgEAoFAxyM2PIFAIBAIBDoeseEJBAKBQCDQ8YgNTyAQCAQCgY7H/wESzZ1D+kZE6QAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 720x72 with 10 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"vW8GmNSwY5Jx"},"source":["## Final Check\n","\n","Make sure all your training results (loss + images) are saved in the notebook. You can run \"Runtime -> Restart and run all...\" to double check before submitting"]}]}